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By Karisa Hamdi

May 29, 2018

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6 Social Media Marketing Failures & What You Can Learn from Them

I think it’s safe to say that social media has taken over (almost) everyone's life.

Think about it; how many times a day do you open Instagram or Facebook casually just to see what your friends are up to? I know I’m on there at least 20 times a day mindlessly scrolling and brands are smart. they’ve caught on to this behavior.

That’s why while you’re scrolling through your friends post you’re likely to see a brand’s pop in there every so often.

Typically they’re easy to pass over, but then there are some that really catch your attention. that’s the whole goal, right?

But, what if they catch your attention for the wrong reason? Like offending your values or making you feel totally alienated from their brand?  

Brands, big and small, make mistakes on social media all the time and in today’s age, they are certainly never forgotten.

When brands put a wedge between themselves and their followers, you can bet everyone is going to be talking about it.

Here are six social media fails for popular companies and how you can avoid making the same mistake for your business:

1. The United States Air Force: Yanny/Laurel

Last week the Yanny/Laurel debate probably took over your social media feed. After listening to this audio clip people began splitting into two teams, those that heard Yanny (my team) and those that heard Laurel.

Brands like Skittles, Warby Parker, and many more shared posts that put themselves into the debate most of them saying things along the lines of “#Yanny or #Laurel... am I the only one that hears BRAND NAME?”

Then the U.S. Air Force came in and really crossed a line.

In a tweet, that has since been deleted, they said  “ The Taliban Forces in Farah city #Afghanistan would much rather have heard #Yanny or #Laurel than the deafening #BRRRT they got courtesy of our #A10 . Read more: https://t.co/pTxpG3X6Ui pic.twitter.com/vLbCg94P3w ”

They took a funny internet debate and brought the death of others into the situation.

You can have different opinions about the war itself, but to bring this insensitive tweet to the Yanny/Laurel debate did not put the brand in a good light.

After getting a ton of backlash, the post was removed and the Air Force issued this apology:

But the lesson still stands: Be careful about what and how you newsjack.

For a sensitive topic like death or even war, you should be wary about how you use humor.

On the flip side, Using a playful meme to bring attention to a much more serious matter can come off badly as well. Always make sure that the news stories you try to align yourself with make sense and are on brand.  

2. Would You Rather: Rihanna

Ever since they updated their layout and upset almost all of their users, Snapchat has been losing popularity.

One feature of the new update is the prominence of more ads. Now, as you’re viewing stories, brands ca interject their ads between each user. Typically, they keep it to brands that are relevant to you, but then there’s this ad for a game called Would You Rather?:

The ad asks users if they’d rather Slap Rihanna or Punch Chris Brown, which directly relates to the domestic abuse case from 2009 where Chris Brown physically assaulted Rihanna.

With users already criticizing Snapchat because of their recent updates, the backlash from this was huge! Snapchat removed the ad and then issued this apology “The advert was reviewed and approved in error, as it violates our advertising guidelines. We immediately removed the ad last weekend, once we became aware. We are sorry that this happened.”

Even with the apology usage of the app is still on the decline and taught us the valuable lesson to be careful of crossing the line between funny and hurtful.

Rihanna even stated that she did not accept the apology issuing the following statement on her Instagram:

Rihanna is a prominent figure in society and making her look bad and then her not accepting the apology caused even more negative backlash against the app. Again, when you’re referencing a very sensitive subject for most users and making light of serious allegations you’re diminishing others feelings.

3. Dove: Transformation

Normally, Dove is known for empowering women and focusing on natural, realistic standards of beauty.

The company has been standing on its Real Beauty platform for over 10 years and, most of the time, it’s proven successful.  Take, for example, successful campaigns like “ Real Beauty Sketches ” and "Evolution" that focus on making women feel proud of the way they look.

Unfortunately, in a recent post on Facebook by the company, they’ve taken a ton of steps backward.

The ad in question shows a black woman transformed into a white woman after using Dove body lotion.

This totally goes against their brand message of saying that all beauty is real beauty by showing a woman transforming into an entirely different race after using their product.

It doesn’t fit with their overall brand messaging and both alienates and insults a large part of their audience.

After receiving backlash from women of all races saying  the ad made them angry and uncomfortable, Dove removed it from Facebook and released this statement:

As we’ve seen with all the other ads, the apology doesn’t do much but the ad does teach us to always review your content through a critical lens. Make sure that it doesn’t offend or in this case, totally go against what your brand represents.

4. Department of Education: W.E.B. DuBois

In this social media fail, the Department of Education sent out a tweet to highlight the importance of education. The tweet itself was great, but see if you can spot the issue below.

Did you catch it? Whoever sent out the tweet misspelled DuBois as DeBois and Twitter went off!

Considering that this was coming from an organization that is supposed to stand for the importance of reading, writing, and arithmetic among other things, this mistake reflected badly on their cause and values.

The Department tweeted an apology but again had a spelling error in the tweet causing everyone on Twitter to do a collective Facepalm.

The lesson learned here is, be careful of your spelling and grammar Always be sure to double check what you’re posting as it can be seen as not only careless but unprofessional. In this case, the typo even took a jab at the organization’s greater platform. So, always proofread!

5. New York Times: Snake People

The New York Times is known for being one of the top newspapers in the world, but that doesn’t mean it’s exempt from some social media fails.

As a top newspaper, you would expect them to be on their game, triple checking all content before publishing. Well, that wasn’t not the case for the article, “ President Trump’s Exaggerated and Misleading Claims on Trade, ”

The editor who worked on the article has a browser extension called "Millennials to Snake People,” which changes the word “millennials” to “Snake People,” which means every time the word millennial was referenced in the above article, it was changed to the phrase snake people causing a lot of people to be utterly confused as they read the article.

The article with the errors was released exclusively online across the company's social channels making it an easy target for criticism.

The New York Times and the editor himself issued an apology on Twitter:

Even big companies that you would expect to have an extreme vetting process in place can fall victim to a social media fail so make sure you learn from their mistakes and double check EVERYTHING.

6. Starbucks: Blonde Espresso

Starbucks has been the center of a couple controversial stories recently, but this campaign is more focused on a less controversial post.

If you entered any Starbucks store late last year, you saw that it was taken over by ads for their new Blonde Espresso. Not only were the stores taken over but their social media accounts as well.

One ad that really stuck out to myself and a lot of others was this one:

I’ve read this post over and over again and it still doesn’t make sense to me.

I get the overall point, but the wording is so confusing and the message doesn’t make any sense for the product. I know I’m not alone because this ad was made fun off all over the internet.

Starbucks has yet to update their content and when you go their site , this post is still front and center.

While they haven’t done anything, learn from their mistake and make sure your ad is clear and effective. Have others outside your company review it before posting to make sure it doesn’t come off like an inside joke or weird riddle.

Key Takeaway

In today’s world, social media can be a highly effective way to increase brand awareness and bring in customers -- but only if it’s used the right way.

As you’re creating your social media campaigns, keep these lessons in mind to make sure you’re not offending your audience or crossing the line into being insensitive towards a wider audience.

If you’re creating something and you question the content or context of the campaign, bring in a couple other opinions otherwise you may see yourself on our next social media fail list.

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132 Social Media Case Studies – Successes and Failures

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That is such a short-sighted and limiting point of view.

Social Media Marketing is not sales – but it can help to sell things. And personally, I have to admit that I have several times bought something, booked an event or took part in something because I saw people (friends and acquaintances OR strangers) talking about it on social media. At the same time, I have never bought anything a salesperson tried to sell me on the phone. So yes, you actually can sell me things on Social Media. And I am not the only person.

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Before you read on - we have various resources that show you exactly how to use social networks to gain massive traffic and leads. For instance, check out the following:

But limiting Social Media Marketing success or failure to the statement: For sales, you need to pick up the phone is simply b%llshi$t. You can use social media for lead generation to fill your sales funnel – but you can also use Social Media for totally different aspects of business like customer management, brand awareness, reputation management, audience building, website traffic and many other things your business can profit from.

Many people do it. I do it and have done so for other projects in the past. The honest answer to “Social Media is not working” is: It is obviously not working the way you are doing it. Try different tactics, learn, adjust, measure, optimize, try something else, try harder, and never stop at “You cannot sell on Social Media!”

So the answer is, yes you can make money with Social Media, but it is not working the same way for each and every business or situation.

Most of the time, if you do not have success with getting ROI out of your Social Media activities, it is not Social Media, which is not working, it is you who are doing something wrong or have the wrong social media strategy.

Social Media cannot simply be done by following a recipe step by step.

That can only get you so far.

In Social Media often the best approaches are already cold coffee when they become common knowledge, and everyone tries to hop on the train. You need to make assumptions, test your assumption, measure success and adjust your marketing strategy according to your results.

Hey, before you read on - we have in various FREE in-depth guides on similar topics that you can download. For this post, check out:

Social media cannot be learned by the book.

But one thing is certain: To shout out sales messages in Social Media is most likely going to fail to give you any return.

What people want and expect from their Social Media activity is so diverse, and there are many Social Media case studies in multiple situations.

Join our  free Email Course  to learn how to start your social media marketing journey:

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Instead of selecting a handful of case studies for this article, I decided to provide you with a list of resources with multiple case studies about how businesses are successfully using social media for their business success.

1.  15 B2B Case Studies for Proving Social Media ROI

Rob Petersen looks at the special situation of using social media platforms to market to businesses instead of consumers. He provides 15 examples ranging from CISCO and Demand Base to LinkedIn and SAP.

2.  50 Social Media Case Studies you Should Bookmark

SimplyZesty looks at a variety of use cases for the different social networks like Facebook, Twitter, Youtube, Pinterest, Instagram and more.

3.  IBM Turns its Sales Staff Social Media Savvy

I love this example as it shows how sales and Social Media Marketing can work hand in hand. Contrary to the above-mentioned comment on our blog, IBM realized that even sales can profit from Social Media with cost-effective leads.

4.  11 Examples of Killer B2B Content Marketing Campaigns Including ROI

Lee Odden of TopRank Marketing focuses more on the Content Marketing side and provides 11 B2B Content Marketing case studies.

5.  B2B Social Media Case Study: How I made $47 million from my B2B blog

This is a personal success story from AT&T’s experience and success with a content strategy.

6.  How ASOS Use Social Media [CASE STUDY]

The story of how the fashion and beauty store ASOS has become Britain’s largest online retailer with the aid of Social Media for ecommerce and online marketing.

7.  5 Outstanding Social Media Campaigns

The examples include the story from a hairdresser who increased sales by 400% without spending a penny. It is not only the big companies who can profit from Social Media.

8.  3 Small Businesses That Found Social Media Success

The examples range from customer service, brand perception to social engagement.

9.  The Best Social Media Campaigns of 2014

These marketing campaigns are more about creating more engagement, generate more fans and increase loyalty amongst audience members for the brand and not so much about direct ROI. Still, they explain how to get it right.

It is not only the social media success stories you can learn from. Sometimes you can learn from other peoples’ failures at least as much as from their successes. Here are some social media case studies on failed social media activities. The failures tend to be on a smaller scale, resulting from bad communication and reactions turning the Social Media conversation in an unwanted direction. It is rare that a company admits to a complete campaign and a ton of money gone down the drain. Still, even from these smaller examples, we all can learn our lessons for our behavior in Social Media:

1.  Social Media Fails: The Worst Case Studies of 2012

The examples are campaign focused and include examples from McDonald’s and Toyota.

2.  19 horrific social media fails from the first half of 2014

These are examples of how you should not communicate in Social Media and showcase some ways you should not copy on how to jump onto trending hashtags and events in Social Media.

3.  5 Big Social Media Fails of 2013 (and What We Learned)

4.  Top 12 Social Media Marketing Mishaps

These are examples of what can happen to you and how a social media Sh$tstorm can brew up. It makes sense to read some of these and talk about possible reactions before any of this kind happens to you. Simply be prepared.

Final Words

I hope you find some useful marketing tips in my little collection of Social Media case studies – or at least, have some fun browsing through these examples. I find them encouraging as they show the variety of cases where Social Media can help your business. And they show how many humans are in Social Media, making it a place where things can go wrong and go well. It is up to you to leverage the full power of social networks and turn the tide.

If you are looking for even more case studies here you go:

Digital Marketing Case Studies

Content Marketing Case Studies

Instagram Marketing Case Studies

Twitter Marketing Case Studies

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Is social media making you unhappy? The answer is not so simple

Senior Lecturer, School of Computer and Mathematical Sciences, University of Adelaide

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Melissa Humphries receives funding from the NIH and the Department of Defence.

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You may have seen headlines that link social media to sadness and depression. Social media use goes up, happiness goes down. But recent studies suggest those findings might not be so straightforward.

Although it is true that people’s feelings of envy and depression are linked to high social media use, there is evidence to suggest social media use may not be causing that relationship. Instead, your mindset may be the biggest thing affecting how social media connects to your wellbeing.

People who feel they are able to use social media, rather than social media “using them”, tend to gain more benefits from their online interactions.

Why do people use social media?

Social media covers a broad range of platforms: social networking, discussion forums, bookmarking and sharing content, disseminating news, exchanging media like photos and videos, and microblogging. These appeal to a wide range of users, from individuals of all ages through to massive businesses.

For some, social media is a way to connect with people we may not otherwise see. In the United States, 39% of people say they are friends with people they only interact with online .

For older people, this is especially important for increasing feelings of connectedness and wellbeing. Interestingly though, for older people, social media contact with family does not increase happiness . Meanwhile, younger adults report increased happiness when they have more social media contact with family members.

Teens, in particular, find social media most useful for deepening connections and building their social networks .

With social media clearly playing such an important role in society, many researchers have tried to figure out: does it make us happier or not?

Does social media make us happier?

Studies have taken a variety of approaches, including asking people directly through surveys or looking at the content people post and seeing how positive or negative it is.

One survey study from 2023 showed that as individuals’ social media use increased, life satisfaction and happiness decreased . Another found that less time on social media was related to increases in work satisfaction, work engagement and positive mental health – so improved mental health and motivation at work.

Comparing yourself to others on social media is connected to feelings of envy and depression. However, there is evidence to suggest depression is the predictor, rather than the outcome, of both social comparison and envy.

All this shows the way you feel about social media matters . People who see themselves using social media rather than “being used” by it, tend to gain benefits from social media and not experience the harms.

Interviews with young people (15–24 years) using social media suggest that positive mental health among that age group was influenced by three features :

• connection with friends and their global community
• engagement with social media content
• the value of social media as an outlet for expression.

There are also studies that look at the emotions expressed by more frequent social media users.

The so-called “ happiness paradox ” shows that most people think their friends on social media appear happier than themselves. This is a seeming impossibility that arises because of the mathematical properties of how friendship networks work on social media.

In one of our studies, Twitter content with recorded locations showed residents of cities in the United States that tweeted more tended to express less happiness .

On the other hand, in Instagram direct messages, happiness has been found to be four times more prevalent than sadness .

How does internet use in general affect our wellbeing?

Some of the factors associated with decreased mental health are not aligned with social media use alone.

One recent study shows that the path to decreased wellbeing is, at least partially, connected to digital media use overall (rather than social media use specifically). This can be due to sleep disruption, reduced face-to-face social interaction or physical activity, social comparison, and cyberbullying. None of these exist for social media alone.

However, social media platforms are known to be driven by recommendation algorithms that may send us down “rabbit holes” of the same type of (increasingly extreme) content. This can lead to a distorted view of the world and our place in it. The important point here is to maintain a diverse and balanced information diet online.

Interestingly, interacting on social media is not the only thing affecting our mental state. Rainfall influnces the emotional content of social media posts of both the user experiencing rain, and parts of their extended network (even if they don’t experience rain!).

This suggests that how we feel is influenced by the emotions in the posts we see. The good news is that happy posts are the most influential, with each happy post encouraging close to two additional happy updates from a user’s friends.

The secret to online happiness therefore may not be to “delete your account” entirely (which, as we have found , may not even be effective), but to be mindful about what you consume online. And if you feel like social media is starting to use you, it might be time to change it up a bit.

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Case Studies About Social Media Marketing and its Effectiveness

Monica Divino

Author & Editor

Content Specialist

Published on: Sep 4, 2023 Updated on: May 16, 2024

Social media marketing offers an exceptional stance insofar as campaigning is concerned. Case studies on utilizing social media marketing can increase brand awareness by showcasing the effectiveness of social media strategies and tactics.

Social media case studies provide evidence of successful social media campaigns that can inspire other brands to adopt similar strategies. Case studies, likewise, provide real-life examples of social media success, which can help build credibility for your brand. Indeed, case studies are a testament to the return on investment that your business can get with the right social media service.

Why is social media marketing important for brands?

According to a recent study , there are around 4.76 billion social media users and that 137 million new users have become online within the past year. Given the sheer number of social media users , it is proof that social media marketing is important for brands.

Keep in mind that a digital marketing agency would conclude that engaging with social media users through social media marketing gives you the opportunity to build trust with potential customers, partners, and employees. Thus increasing your brand awareness and reach since social media allows for easy and effective brand building.

55% of people learn brands through social media

In this technological age, it is a common place for people to discover products, services, or companies through social media. In fact, according to a report , 55% of consumers learn about companies and brands through social media. So, capitalize on this growing network and make a strong social media presence to enable your audience to engage with you and ultimately build customer loyalty.

79.7% of people make purchases based on online or social media advertisments

Statistics show that 79.7% of people make purchases based on online or social media advertisements. This implies that a significant number of users are influenced by social media advertisements. A strong social media presence presents an incredible opportunity to proclaim your brand, increase brand awareness, and invite new and potential customers.

How to measure social media marketing effectiveness?

Measuring the effectiveness of your social media marketing strategies is crucial for optimizing your campaigns, refining your targeting, and achieving your marketing goals. It also allows you to evaluate your ROI, stay competitive, and make data-driven decisions that can help you achieve success on social media platforms. That said, here are five ways to measure social media marketing effectiveness.

You can determine the audience reach of your content using the “ reach ” measure. In other words, it shows how many people have already seen your publication once. Therefore, care should be used when utilizing reach as a success statistic. This is due to the reach metric's frequent usage of estimates. The advantage of this, though, is that it enables you to estimate the size of your possible audience. A reach of 10,000, for instance, indicates that 10,000 individuals will at least once view your publication in their news feed.

2. Impressions

Impressions reflect how frequently your publication has been displayed on screens. The same person can view this content many times. In the preceding example, if your reach was 1,000 and you had 10,000 impressions, you could conclude that users had viewed the publication 10 times.

3. Social media mentions

The number of times a person or influencer has cited your work is referred to as the number of mentions . This is one method of expanding your audience. Getting frequently cited may indicate that the quality of your article is appreciated. For instance, the @personname function is used when a user or influencer mentions you in a post or shares your material. They will let you know that they've mentioned you.

4. Customer service

Having good customer service ensures that you can build a strong reputation and culture among your business. Providing excellent customer service always helps you keep clients. Keeping consumers improves revenue and is also far less expensive than trying to acquire new ones.

Retention through customer service is one of the most important factors that could measure your social media marketing effectiveness. If your customers are more likely to complete a transaction or purchase because of good customer service brought by your social media marketing, then this shows that your social media marketing is effective.

5. Sentiment analysis

Sentiment analysis can be a useful tool for measuring social media effectiveness by providing insights into how people feel about a brand, product, or service. By tracking sentiment over time, comparing to competitors, identifying influencers, and measuring customer satisfaction, brands can make data-driven decisions and optimize their social media strategies for better results.

There are many ways to measure your social media marketing effectiveness. By tracking metrics such as engagement, reach, brand mentions, and sentiment analysis you can determine the impact of your social media campaigns. Indeed, determining social media effectiveness is what social media marketing case studies use to look into benchmarks for their successful social media campaigns.

8 social media case studies that you need to look into

It's always a good idea to benchmark your social media marketing with those made by other brands. This benchmarking is done through social media marketing case studies. These case studies allow you to take in input and apply them to your own accounts. This way, you can achieve similar, if not better, results.

That said, let us look into 8 social media case studies that you need to look into.

1. Airbnb’s Wonderlust Showcase

Airbnb’s social media marketing campaign utilized Instagram to showcase unique and inspiring photos of their rental properties around the world. By sharing stunning photos that inspired wanderlust in their followers, they were able to increase brand awareness and drive bookings.

The effect of this social media campaign garnered Airbnb over 6.7 million followers on instagram , resulting in an increase in Airbnb’s engagement rate of 1.5% on Instagram , which is higher than the average engagement rate for the travel industry.

Campaign : Airbnb’s Wonderlust Showcase Platform : Instagram Campaign outline : Airbnb’s success is attributable to its effective use of Instagram in showcasing stunning and inspiring photos of its rental properties around the world. What worked? Airbnb's social media campaign was successful because it effectively used visual storytelling, user-generated content, authenticity, and consistency to create a strong and engaging social media presence. By showcasing the unique value of their brand on Instagram, they were able to increase brand awareness and drive bookings.

2. Make-A-Wish Foundation’s Share Your Ears

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Make-A-Wish Foundation was able to increase its social media reach, audience, and engagement when it partnered with Disney in a “ Share Your Ears” campaign .. The strategy of this social media campaign is rather straightforward: Ask people to take a photo of themselves wearing Mickey Mouse ears, post it on social media with a hashtag #ShareYourEars. After that, a $5 donation would be made to the “Make-A-Wish” foundation.

This social media marketing campaign resulted in over 1.7million photos posted and 420 million social media impressions ., which ultimately led to a total increase of 330% in social media reach and a 554% increase in engagement during the campaign.

Campaign : Share Your Ears Platform : Twitter Campaign outline : Take a pic with Mickey Mouse ears, then post it with #ShareYourEars. What worked ? The marketing strategy worked because it relied on the people to post or advertise for the Make-A-Wish foundation. This not only increased the reach of the brand, but also made it organic such that user engagement was prioritized.

3. Nike’s #BetterForIt

Nike created the #BetterForIt Campaign on social media, targeting women with inspiring messages about health and fitness. They used a combination of social media platforms, including Instagram, Twitter, and Facebook, to promote the campaign and encourage women to share their own fitness stories.

The campaign was a huge success, reaching over 800,000 retweets on Twitter, with Nike’s Instagram account gaining over 50,000 new followers within just a week following the campaign. Accordingly, the success of this campaign is shown when Nike expected to garner over $2billion additional sales in 2017.

Campaign : #BetterForIt Platforms : Instagram, Twitter, Facebook Campaign outline : Nike's #BetterForIt campaign was a marketing initiative that aimed to inspire and motivate women to embrace fitness and become more active. Its goal was to encourage women to participate in fitness activities. What worked ? Nike's #BetterForIt campaign was successful because it effectively targeted its audience, delivered an inspiring message, used a multi-channel approach, leveraged influencer marketing, and used data and analytics to optimize its approach. By doing so, they were able to create a campaign that resonated with women and helped to build brand loyalty and engagement.

4. Marketing 360’s Social Media Case Study

This case study example from Marketing 360 illustrates the potency of a Facebook ads sales funnel for B2B marketing. A series of social media advertising that targets a particular audience at each stage of the buying process is known as an ads funnel.

You may direct new leads through the sales funnel and convert them into paying customers by outlining the buyer's journey and developing a social media marketing ad campaign for each stage. A truck lift manufacturer saw a 235% boost in conversions as a result of this social media strategy.

Marketing 360: Facebook Ad Funneling Platform: Facebook What worked: Through the use of Facebook Ad Funneling, Marketing 360 was able to increase its conversion rate by 235% thus increasing sales leads and turning them into paying customers.

5. Coca Cola’s #ShareACoke

Coca-Cola created a Share a Coke campaign, where they printed popular names on their soda bottles and encouraged people to share photos on social media with the hashtag #ShareACoke.

• The campaign generated over 500,000 photos shared on social media using the hashtag #ShareACoke.
• Sales volume for Coca-Cola increased by 2.5% during the campaign period.
• Coca-Cola's Facebook page received a whopping 870% increase in traffic during the campaign period.

Campaign : #ShareACoke Platform : Twitter, Facebook, and Instagram What Worked? Coca-Cola's "Share a Coke" campaign was successful because it personalized Coke bottles and cans with customers' names, encouraged social media sharing using the hashtag #ShareACoke, tapped into emotions, had a global reach, and resulted in a sales increase of 2.5%. These factors, among others, contributed to the campaign's success in engaging with customers and increasing brand loyalty.

6. Old Spice’s “The Man Your Man Could Smell Like”

In 2010, Old Spice launched a viral campaign on social media called "The Man Your Man Could Smell Like." The campaign featured a series of humorous videos featuring actor Isaiah Mustafa. The campaign was a huge success and resulted in a 107% increase in Old Spice sales. During this campaign, Furthermore, Old Spice’s Twitter following increased by 2,700% . This campaign likewise generated over 1.4 billion social media impressions during the first week.

Campaign : The Man Your Man Could Smell Like Platform : Twitter, Youtube, and Facebook Campaign outline : The 2010 Old Spice social media marketing campaign, known as "The Man Your Man Could Smell Like," was a multi-faceted campaign that leveraged various social media platforms to engage with consumers and drive sales. What worked? The 2010 Old Spice social media marketing campaign worked because it was innovative, engaging, and effective at driving sales and building brand awareness. The campaign demonstrated the power of social media as a marketing tool and set a new standard for other brands to follow.

7. Dove’s “Real Beauty Sketches”

In 2013, Dove launched a campaign on social media called " Real Beauty Sketches ." The campaign featured a forensic artist who drew sketches of women based on their own descriptions of themselves and then drew sketches of the same women based on descriptions from other people. The campaign was a huge success, and the video has been viewed over 163 million times .

• The video was shared widely on social media, with over 4.6 million shares on Facebook and Twitter .
• Increased brand awareness. The campaign helped to increase Dove's brand awareness, with a 30% increase in sales in the first six months after the campaign's launch.

Campaign : Real Beauty Sketches Platform : Twitter, Facebook, and Youtube Campaign outline: Dove's "Real Beauty Sketches" campaign, launched in 2013, aimed to challenge conventional standards of beauty and promote self-confidence among women. What worked? Dove’s "Real Beauty Sketches" campaign resonated with people because of its emotionally engaging content, unique approach, and positive message. It was a powerful example of how a brand can use its platform to promote a positive message and drive social change.

8. Wendy’s #NuggsForCarter

In 2017, Wendy's launched a Twitter campaign called " #NuggsForCarter ." The campaign began when a teenager named Carter Wilkerson asked Wendy's how many retweets he needed to get free chicken nuggets for a year. Wendy's responded with a challenge: 18 million retweets.

The campaign received over 3.43 million retweets - making it the most retweeted tweet of all time at the time of the campaign. The campaign likewise generated a significant amount of engagement for Wendy's on social media, with the brand receiving thousands of tweets and mentions from users participating in the campaign. dium.com Campaign : “NuggsForCarter” Platform : Twitter What Worked ? Wendy’s Social media campaign generated a significant amount of engagement #NuggsForCarter campaign was a fun and engaging way for Wendy's to connect with its audience on social media. The campaign's lighthearted and humorous tone resonated with users, generating widespread engagement and media attention.ent for Wendy's on social media, with the brand receiving thousands of tweets and mentions from users participating in the campaign.

The right move

The role of social media marketing is that it allows marketers to connect and interact with potential customers on social media sites like LinkedIn, Twitter, Youtube, Facebook, or Instagram. Marketers can engage their audience with a solid social media strategy and the ability to provide interesting content. Here are the thre reasons why engaging in social media marketing is the right move for your company.

1. Increases brand awareness

Social media marketing can help you personalize your business while fostering loyalty, respect, trust, and authority. This is so because social media marketing increases your brand credibility and trustworthiness . through the publication of materials that highlight the customers that actually use your brand.

2. Boosts website traffic

Ideally, your social network post should direct visitors to your website, which is most likely where they will wind up. By considering your social media sales funnel, your lead can locate your material on a social media platform and ultimately visit it for more information. As what was done in the case of “Marketing 360”. In doing this, you can draw visitors to your website, thus increasing website traffic.

3. Improved brand loyalty

Your customers will find you and connect with you more easily if you have a social media presence. You are more likely to increase client loyalty and retention by interacting with your customers on social media. Considering one of the primary objectives of practically any business is to build a loyal customer base. Brand loyalty and customer happiness frequently go hand in hand.

Key takeaway

While social media marketing may be beneficial to your company, you must first know how to take advantage of this strategy. This knowledge may be sourced from studying and applying case studies of successful marketing strategies. By benchmarking your marketing strategies from unique and successful marketing campaigns, you can boost not only brand awareness but also customer engagement.

That said, here are some important and digested takeaways that you can take with you should you decide to engage in social media marketing:

• Always ensure that your social media marketing is effective. Social media marketing campaigns entail time and resources to implement. So, you have to make sure that your social media marketing is effective by considering factors such as: reach, impressions, mentions, and your customer service.
• Consider Studying Successful Social Media Campaigns. By studying and analyzing how some social media campaigns became successful, you can benchmark and pattern your social media campaigns from them. This allows you to not only have some parallelism with their successful campaign, but also garner any benefits that may result from what has been tried, tested, and proven.
• Engage in social media marketing. Through social media marketing, you can increase brand awareness, brand reach, and customer engagement. This is because of the unique features of social media platforms as well as the sheer number of social media users.

If you have any questions or inquiries, reach out to us on Facebook , X , or LinkedIn , and we’ll be happy to assist you in your app campaigns.

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Top 3 Social Media Case Studies to Inspire You in 2024

Discover three successful social media case studies from top brands and learn how to create one. Benefit from their strategies and mistakes to ensure the success of your next campaign.

Social media is every marketer’s safe haven for branding and marketing.

And why not?

More than 50% of the population is active on social media, and more are signing up with every passing second.

In a recent poll by HubSpot, 79% of the respondents have made a purchase after seeing a paid advertisement on social media .

This isn’t just a happenstance.

It’s the constant efforts that these brands put behind their dynamic presence on social media, that counts.

But how do they captivate their customers’ attention for this long despite the budding competitors?

Well, that’s something that we’ll reveal in this blog.

We shall assess 3 different social media case studies by top brands who are best in their niches. Their game is simple yet effective.

How effective? Let’s take a look.

Social Media Case Study 1: Starbucks

Starbucks and social media are a match made in heaven. Being one of the sensational brands online, they are stirring the social media world with their strong presence.

They brew the right content to elevate the experiences of their coffee lovers. But how do they nail marketing with perfection every single time? Let’s find out.

Starbucks in Numbers

Starbucks mastered the advertising transition from offline fame to online undertaking. They use each social media with a varied goal to target pitch-perfect reach. Drawing in more customers than ever before, they strike the right balance in content across multiple platforms.

Key Takeaways

Though not every company has a Starbucks budget to promote and spend lavishly on social media marketing, here are some quick takeaways that will undoubtedly help.

1. Chasing Trends

Be it any event, brands must take the advantage to showcase their viewpoints and opinions. Successful brands like Starbucks jump into the bandwagon and leave no stone unturned to make their voice count in the trending list.

Here’s one such social media campaign example from Starbucks.

Starbucks is a firm believer in LGBTQ+ rights. When the pride wave surged, Starbucks came forward and reinstated its belief through the #ExtraShotOfPride campaign.

Starbucks joined hands with the Born This Way Foundation to raise $250K to support the LGBTQ+ community. Throughout the social media campaign, they shared quotes and stories of various Starbucks employees cherishing the pride spirit.

2. Less is More

Social media is not about quantity but quality. Starbucks follows the “less is more” principle to maintain the quality standards, even in the caption. Spamming followers’ feeds with constant posting is a big no-no. Starbucks shares 5-6 posts per week on Instagram and 3-4 weekly posts on Facebook .

Creative and crisp! That’s what defines a Starbucks caption. This post with 111+k likes is no exception. Nothing is better than a minimalist post with a strong caption.

3. User Generated Content is the King

Ditch the worry of creating content every day when you can make use of user generated content. Starbucks makes sure to retweet or post its loyal customers’ content. User generated content postings starkly improve brand credibility.

Look at this Facebook post made out of customers’ tweets. The new Oatmilk drink got the appreciation shower by some, and Starbucks couldn’t resist but share it with others. It saved them efforts on content brainstorming, plus they got free PR.

4. Building Rapport

Building rapport with the audience is an unsaid rule to brand fame. Social media has now taken the onus of dispensing quality service by aiding brands in prompting faster replies .

Starbucks is always on its toe to respond to customers actively solving concerns, expressing gratitude, or reposting. That kind of proactive service definitely deserves love and adoration.

5. Loads of campaigns

Starbucks is known for its innovative social media campaigns. Be it a new product launch or any festivity around the corner, Starbucks always turns up with a rewarding campaign.

In this social media campaign example, Starbucks introduced #RedCupContest with prizes worth $4500 during Christmas of 2016. A new entry came every 14 seconds.

The grand total of entries was a whopping 40,000 in just two days. Indeed Starbucks knows how to get the most out of the festive fever.

6. Content mix

Last but not least, the content mix of Starbucks is inspiring. They create tailored content for every platform.

The official youtube channel of Starbucks comprises content in varied hues. From recipes to even series, Starbucks is the ultimate pioneer of experimenting.

Even on Instagram, they use all the features like Guides, Reels, and IGTV without affecting their eye-popping feed. Starbucks also follows the design consistency for its aesthetic content mix.

Starbucks has proved time and again to be a customer-centric brand with their unrelenting efforts.

Social Media Case Study 2: Ogilvy & Mather

Ogilvy & Mather needs no introduction. Founded by David Ogilvy, the ‘Father of Advertising’ in 1948, the agency continues the legacy of revolutionizing marketing long before the advent of social media.

The iconic agency helps several Fortune 500 companies and more make a massive impact on their audiences worldwide.

Ogilvy & Mather knows its game too well and never fails to astonish. Not just high-profile clients, Ogilvy nails its marketing with perfection every single time.

Keep on reading.

Ogilvy & Mather in Numbers

They use social media to target pitch-perfect reach. Drawing in more hype than ever before, they know how to strike the right balance and bring out emotions with their heart-warming campaigns.

Not every company has David Ogilvy’s legacy or even affluent clients to boast of, but here are some quick takeaways that will undoubtedly help you become a pro marketer.

1. Integrating Values

Ogilvy stands apart from the crowd, creating trends. They leave no stone unturned to communicate values.

Proud Whopper is one such social media campaign by Ogilvy that was an instant hit on the internet. People were offered whoppers in rainbow-colored wrappers, with a note that said, “Everyone’s the same on the inside.” This was to reinstate the importance of LGTQ+ rights.

The campaign got 1.1 billion impressions, $21 million of earned media, 450,000 blog mentions, 7 million views, and became the #1 trending topic on Facebook and Twitter.

Ogilvy made a remarkable #Tbt video to honor this momentous event showcasing their supremacy in creating impactful campaigns.

2. Quality over Quantity

Ogilvy believes in the “ Quality supremacy ” to maintain their high standards, even in post captions.

Arbitrary posting isn’t a part of their agenda. They share 5-7 posts on Instagram and Facebook weekly.

Direct and very precise. That’s what defines an Ogilvy caption. This post is no exception. They have exhibited the success of their client work by describing the motive behind the campaign and sharing the ad they created for raising awareness.

3. Adding Credibility

Won awards? It’s time to boast! Because that’s the most authentic way of establishing trust among your clients. It bears proof of your excellence.

Look at this pinned Twitter post. Ogilvy won the Global Network of the Year by the very prestigious London International Awards. It also earned Regional Network of the year for Europe, the Middle East, Asia, and Europe.

What better than this to give its audience an idea about Ogilvy’s roaring success and undoubted potential?

4. Being Innovative

Building rapport with the audience is an unsaid rule to brand fame. And that’s why you need to tell stories. Social media has become an indispensable medium to spread your stories far and wide.

Ogilvy shares its historical tale of existence and how it has adapted to the challenges of the changing world. The team extensively talks about their adaptation to the latest trends to stay on top always.

5. Brainstorming Uniqueness

Being unique is what propels you on social media. People are always looking for brands that do something different from the herd. So your task each day is undeniably brainstorming unique content.

KFC wanted more of its customers to use its app. Well, Ogilvy and KFC decided to hide a secret menu in the app, which was a mass invitation for the download without being salesy at all. Results? Downloads up by 111% at launch!

6. Inspire Your Peeps

Inspiration is everywhere. But how do you channelize and mold it as per your brand guidelines? The renowned brands move their audience, filling them with a sense of realization. Who doesn’t seek validation? We all need quotes and inspiration to live by.

Ogilvy has dedicated its entire Pinterest profile to inspiration. The profile has numerous insightful infographics that encourage you to pursue marketing when your spirits run low. And that’s how it brings out the very essence of being the marketing leader: by inspiring its followers.

Got some good ideas for your branding? We have created templates and tools to help you execute them hassle-free. Tread on further and download the Trending Hashtag Kit for 2024 to get into action.

Social Media Case Study 3: PewDiePie

YouTube king with 111 Million subscribers on PewDiePie Channel, Felix Arvid Ulf Kjellberg, has defied all norms. One of the most prolific content creators of the decade, Felix was on the list of World’s 100 Most Influential People by Time Magazine in 2016.

Needless to say, he is still relevant to this day and has a massive following on social media. Not just for branding, the Swedish YouTuber leveraged social media to give himself a new identity and opened doors to fame and a successful career.

What was the cause of this extraordinary trajectory?

Let’s find out.

PewDiePie in Numbers

PewDiePie likes to keep his social media raw and unfiltered. That’s why subscribers love to have a glimpse of his everyday life and follow him on other social media platforms as well. Here’s a quick snapshot of that.

Felix took the early bird advantage and started creating content when it wasn’t even popular practice. We can’t go back in time, but we can definitely learn a lot from his social media success.

1. Start Now

If you are still skeptical about making the first move, then don’t. Stop waiting and experiment. It’s better late than never.

Social media is in favor of those who start early because then you create surplus content to hold your audience . You quench their thirst for more quality content.

PewDiePie started creating videos in 2011 and live-streamed his gaming sessions with commentaries. It was something new and completely original. Ever since, he has continued to make thousands of videos that entertain his audience.

2. Gather Your Tribe

Being a content creator, PewDiePie knows his act of engaging his audience very well. He strives to build lasting connections and encourages two-way communication. As a result, his followers like to jump onto his exciting challenges.

Felix treasures his gaming community. He frequently asks his followers to take screenshots and turn them into funny memes . He gives them tasks to keep them engaged and amused .

3. Collaboration and Fundraising

Once you reach the stage and gain popularity, people want to see more of you with their favorite personalities. That’s what Felix does.

He collaborates with multiple YouTubers and brands and puts out exclusive content for his followers. He also goes for multiple fundraising campaigns to support vital causes and social wellbeing.

Here’s one such social media campaign example. PewDiePie supported the CRY foundation and raised $239000 in just one day to bring a positive impact for children in India. He thanked all for their contribution and taking active participation towards a noble cause.

4. Keep it Real

Felix likes to keep his content fluff-free. You get to witness raw emotions from an unfiltered life. This instantly appeals to the audience and makes the posts more relatable .

Apart from that, he also uses storytelling techniques to narrate his experiences, adding a very personalized touch to each of the videos.

Here’s a video of Felix where he and Ken from CinnamonToastKen discuss what can be possibly done with a million dollars around the world. The topic is quite intriguing.

More than 3.8M people have watched it and 216K of them liked it as well, proving that you need not always sweat to create complex content. Even the simplest ones can make the cut.

How to Write a Social Media Marketing Case Study

Many small businesses struggle when it comes to social media marketing. But guess what? Small businesses can slay the competition with a powerful tool: the social media case study.

These social media case studies are success stories that prove your hustle is paying off. Here’s how to weave a case study that showcases your small business wins:

Building Your Brag Book

• Pick Your Perfect Project:  Did a specific social media campaign drive a surge in sales? Highlight a product launch that went viral. Choose a project with impressive results you can showcase.
• DIY Interview:  Don’t have a fancy marketing team? No worries! Record yourself talking about your challenges, goals, and the strategies that made a difference.
• Data Dive:  Track down social media analytics! Look for growth in followers, website traffic driven by social media, or engagement metrics that show your efforts are working.

Now that you have all the ingredients, it’s time to cook a brilliant case study

Crafting Your Case Study

• Headline Hunt:  Grab attention with a clear and concise headline. Mention your business name and a key achievement (e.g., “From 100 to 10,000 Followers: How We Grew Our Bakery’s Social Buzz”).
• Subheading Scoop:  Briefly summarize your success story in a subheading, piquing the reader’s interest and highlighting key takeaways.
• The Business Struggle:  Be honest about the challenges you faced before tackling social media. This will build trust and allow other small businesses to connect.
• DIY Social Strategies:  Share the social media tactics you used, such as engaging content formats, community-building strategies, or influencer collaborations.
• Numbers Don’t Lie:  Integrate data and visuals to support your story. Include charts showcasing follower growth or screenshots of top-performing posts.
• Simple & Straightforward:  Use clear, concise language that’s easy to understand. Bullet points and short paragraphs make your case study digestible and showcase your professionalism.

Remember: Your social media case study is a chance to celebrate your achievements and build businesses. So, tell your story with pride, showcase your data-driven results, and watch your brand recognition soar

Social media campaigns are winning hearts on every platform. However, their success rates largely depend on your year-round presence. That’s why being consistent really does the trick.

We’re sure you must have learned a few things from the above-mentioned social media case studies .

To excel further at your social media marketing, use our FREE Trending Hashtag Kit and fill your calendar with everyday content ideas.

On downloading, you get 3000+ hashtags based on each day’s theme or occasion. You also get editable design templates for hassle-free social media posting.

What are you waiting for? Download now.

Frequently Asked Questions

🌟 How do I start a social media campaign idea?

Here’s how you can start a social media campaign:

• Finalize your campaign goals
• Brainstorm personas
• Pick a social media channel
• Research your competitors and audience
• Finalize an idea that’s in trend
• Promote the campaign
• Start the campaign
• Track the performance

🌟 What are the different types of social media campaigns?

Different types of social media campaigns are:

• Influencer Campaigns
• Hashtag Challenges

🌟 Why is social media campaign important?

Social media campaigns have various benefits:

• Boost traffic
• Better Conversions
• Cost-effective Marketing
• Lead Generation
• PR & Branding
• Loyal Followers

🌟 What are some of the best social media campaign tools?

Some of the best social media campaign tools are:

• SocialPilot

🌟 What are the top social media sites?

The top social media sites are:

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eTable. Misinformation vs Public Health Guidelines

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• Errors in Quotes and Dates JAMA Network Open Correction October 25, 2023

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Sule S , DaCosta MC , DeCou E , Gilson C , Wallace K , Goff SL. Communication of COVID-19 Misinformation on Social Media by Physicians in the US. JAMA Netw Open. 2023;6(8):e2328928. doi:10.1001/jamanetworkopen.2023.28928

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Communication of COVID-19 Misinformation on Social Media by Physicians in the US

• 1 Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts, Amherst
• Correction Errors in Quotes and Dates JAMA Network Open

Question   What types of COVID-19 misinformation have been propagated online by US physicians and through what channels?

Findings   In this mixed-methods study of high-use social media platforms, physicians from across the US and representing a range of medical specialties were found to propagate COVID-19 misinformation about vaccines, treatments, and masks on large social media and other online platforms and that many had a wide reach based on number of followers.

Meaning   This study’s findings suggest a need for rigorous evaluation of harm that may be caused by physicians, who hold a uniquely trusted position in society, propagating misinformation; ethical and legal guidelines for propagation of misinformation are needed.

Importance   Approximately one-third of the more than 1 100 000 confirmed COVID-19–related deaths as of January 18, 2023, were considered preventable if public health recommendations had been followed. Physicians’ propagation of misinformation about COVID-19 on social media and other internet-based platforms has raised professional, public health, and ethical concerns.

Objective   To characterize (1) the types of COVID-19 misinformation propagated by US physicians after vaccines became available, (2) the online platforms used, and (3) the characteristics of the physicians spreading misinformation.

Design, Setting, and Participants   Using US Centers for Disease Control and Prevention guidelines for the prevention and treatment of COVID-19 infection during the study window to define misinformation, structured searches of high-use social media platforms (Twitter, Facebook, Instagram, Parler, and YouTube) and news sources ( The New York Times , National Public Radio) were conducted to identify COVID-19 misinformation communicated by US-based physicians between January 2021 and December 2022. Physicians’ state of licensure and medical specialty were identified. The number of followers for each physician on 4 major platforms was extracted to estimate reach and qualitative content analysis of the messages was performed.

Main Outcomes and Measures   Outcome measures included categories of COVID-19 misinformation propagated, the number and traits of physicians engaged in misinformation propagation, and the type of online media channels used to propagate misinformation and potential reach.

Results   The propagation of COVID-19 misinformation was attributed to 52 physicians in 28 different specialties across all regions of the country. General misinformation categories included vaccines, medication, masks, and other (ie, conspiracy theories). Forty-two physicians (80.8%) posted vaccine misinformation, 40 (76.9%) propagated information in more than 1 category, and 20 (38.5%) posted misinformation on 5 or more platforms. Major themes identified included (1) disputing vaccine safety and effectiveness, (2) promoting medical treatments lacking scientific evidence and/or US Food and Drug Administration approval, (3) disputing mask-wearing effectiveness, and (4) other (unsubstantiated claims, eg, virus origin, government lies, and other conspiracy theories).

Conclusions and Relevance   In this mixed-methods study of US physician propagation of COVID-19 misinformation on social media, results suggest widespread, inaccurate, and potentially harmful assertions made by physicians across the country who represented a range of subspecialties. Further research is needed to assess the extent of the potential harms associated with physician propagation of misinformation, the motivations for these behaviors, and potential legal and professional recourse to improve accountability for misinformation propagation.

As of May 11, 2023, an estimated 1 128 000 COVID-19 deaths had occurred in the US, 1 and nearly 14% of people infected by the COVID-19 virus have experienced the post–COVID-19 condition. 2 , 3 As of December 2022, estimated death rates for unvaccinated persons in the US were 271 per 100 000 compared with 82 per 100 000 for those fully vaccinated, yet only 69.2% of eligible people had received the full primary vaccine series, and 15.5% had received the bivalent booster. 1 Vaccination rates have varied by region throughout the pandemic despite widespread availability, with southeastern states having lower full primary series rates (52%) compared with northeastern states (80%). 1 Other preventive behaviors, such as mask wearing and social distancing, have varied similarly by geographic region. 4 , 5

Individual health behaviors related to COVID-19 have been attributed to complex social phenomena, including inconsistent recommendations by government entities early in the pandemic, mistrust of the scientific community, political polarization, and unclear or incorrect guidance from other sources. 6 - 8 COVID-19 misinformation, defined as false, inaccurate, or misleading information according to the best evidence available at the time, and disinformation, defined as having an intentionally malicious purpose, have been ubiquitous on social media, despite major platforms’ COVID-19 misinformation policies. 9 Medical misinformation was propagated long before the COVID-19 pandemic, 10 but the internet increases reach and speed of dissemination, potentially exacerbating misinformation consequences during an unparalleled public health threat that has killed more than 7 million people across the globe. 11 - 13

COVID-19 misinformation has been spread by many people on social medial platforms, 14 but misinformation spread by physicians may be particularly pernicious. 15 Physicians are often considered credible sources of medical and public health information, increasing the potential negative impact of physician-initiated misinformation. The US Food and Drug Administration (FDA) and others have called for action to limit the potential harm of physician-propagated COVID-19 misinformation. 15 , 16 Despite the rising concerns voiced in news articles and opinion pieces, physician-propagated COVID-19 misinformation and its associated outcomes remain understudied.

This study aimed to address this gap in knowledge by examining COVID-19 misinformation communicated on social media platforms and other online sources by US physicians after vaccines were made available. Understanding the extent of this phenomenon, its potential impact, and associated professional, ethical, and legal ramifications may help to better understand the role that physician-propagated COVID-19 misinformation may have played in preventable COVID-19 deaths and mistrust in institutions.

This mixed-methods study sought to characterize the (1) type of COVID-19 misinformation physicians communicated online between January 1, 2021, and May 1, 2022; (2) social media and other online platforms where misinformation appeared; and (3) characteristics of the physicians. Physician age, sex, and race and ethnicity were not available on social media or other online postings. A decision was made to not infer these data from pictures or other means to avoid potential bias and misclassification. We defined COVID-19 misinformation as assertions unsupported by or contradicting US Centers for Disease Control and Prevention (CDC) guidance on COVID-19 prevention and treatment during the period assessed or contradicting the existing state of scientific evidence for any topics not covered by the CDC (eTable in Supplement 1 ). We conservatively classified inaccurate information as misinformation rather than disinformation because the intent of the propagator cannot be objectively assessed. The University of Massachusetts Institutional Review Board determined that this study did not meet criteria for human participant research. This study followed the Standards for Reporting Qualitative Research ( SRQR ) reporting guidelines.

First, we conducted structured searches of social media platforms and general web searches in late spring of 2022 to identify media containing COVID-19 misinformation attributed to US-based physicians, defined as using doctor of medicine (MD) or doctor of osteopathic medicine (DO) after their name and being licensed to practice medicine in the US at some time or never licensed but working in the US. The start date was selected in relation to the availability of the COVID-19 vaccines. Search terms included the following: “COVID,” “vaccine,” “doctor” or “physician,” “ineffective,” “pharmaceutical,” “medication,” “ivermectin,” “hydroxychloroquine,” and “purchase.” Search terms were refined based on initial searches to include “COVID misinformation,” “doctor” or “physician,” and/or “conspiracy theory.” Conspiracy theories were defined as communicating skepticism of all information that does not fit the theory, overinterpreting evidence that fits the theory, and/or evidence of internal inconsistency. 17 The platforms searched were selected based on the volume of news articles, popularity, and searchability (Instagram, Twitter, YouTube, Facebook, Parler, TikTok, The New York Times , National Public Radio) 18 ; if the findings on one platform indicated that another platform could have additional new data, it was added to the search list. Due to the large volume and repetitiveness of Tweets, Twitter searches focused initially on America’s Frontline Doctors’ Twitter profile because of the volume of COVID-19 misinformation in its Tweets, 19 its large following, and the potential for physicians propagating misinformation to follow the page. Followers of the America’s Frontline Doctors’ page with an MD or DO in their header were traced on Twitter and other platforms as well. General internet searches using Google’s search engine were conducted to identify misinformation attributed to physicians in third party platforms, such as local news articles.

The following information was collected from each source: physician’s name, medical specialty, the state(s) in which they were currently or had been licensed, whether their license to practice was active, had lapsed, or been revoked based on state medical board site searches, when the misinformation was posted (if available), from what source it was found, and the number of followers the physician had (if the source was a social media platform). Misinformation was classified into the following categories: medication, vaccine, mask/distancing, and other unsubstantiated or false claims. After the initial searches were completed, the physicians’ names were searched on the social media platforms and through general online searches to identify misinformation they posted that may have been missed in the initial searches and extended through December 2022.

Descriptive statistics were used to quantify the types of misinformation, the frequency in which they appeared, the platforms on which they were found, and characteristics of the physicians identified (eg, specialty and state[s] in which the physician was licensed). We calculated the total, median, and IQR for the number of followers on platforms with the highest volume of users (Twitter, Facebook, YouTube, Instagram) using Stata software, version 17 (StataCorp).

We performed directed qualitative content analysis 20 of the misinformation using a validated rapid qualitative analysis approach. 21 The analytic team (S.S. and M.D.) populated a templated summary table with misinformation text extracted from each media platform. The team divided the physician list and generated a summary of the misinformation associated with each of the physicians. In the second step of this analytic process, each team member individually identified pertinent and common themes, subthemes, and supporting quotes for each. After this was done individually, the team met to discuss their findings and combine the findings into a final list of themes and subthemes. Considerations regarding reflexivity included that S.G. is a public health professor and physician, and M.D. and S.S. are aspiring physicians, which may have increased sensitivity to potential harms.

A total of 52 US physicians were identified as having communicated COVID-19 misinformation in the period assessed. All but 2 were or had been licensed to practice medicine in the US; the others were researchers. The 50 physicians who currently were or had been licensed represented 28 distinct medical specialties (3 of 50 had 2 different specialties; primary care was the most common overall [18 (36.0%)]) and they were licensed or working in 29 states across the US ( Figure and Table 1 ). Forty-four of the 50 physicians (88.0%) held an active license in at least 1 state; 3 (6.0%) did not have an active license, 4 (8.0%) had had a license suspended or revoked, and 1 (2.0%) had active licenses in 2 states and revoked/suspended licenses in 2 other states. Nearly one-third (16 of 52) were affiliated with groups with a history of propagating medical misinformation, such as America’s Frontline Doctors. Specific types of misinformation included the following: (1) vaccines were unsafe and/or ineffective, (2) masks and/or social distancing did not decrease risk for contracting COVID-19, (3) medications for prevention or treatment were effective despite not having completed clinical trials or having been FDA approved, and (4) other (eg, conspiracy theories).

Most of the 52 physicians (40 [76.9%]) who posted misinformation did so in more than 1 of the 4 categories identified. Vaccine misinformation was posted by the majority (42 [80.8%]), followed by other misinformation (28 [53.8%]; eg, government and public health officials deliberately falsified COVID-19 statistics) and medication misinformation (27 [51.9%]).

Of these 52 physicians, 20 (38.5%) posted COVID-19 misinformation on 5 or more different social media platforms and 40 (76.9%) appeared on 5 or more third-party online platforms such as news outlets. Twitter was the most used platform, with 37 of the 52 physicians (71.2%) posting misinformation and a median of 67 400 followers (IQR, 12 900-204 000). Additional details of physicians’ reach by platforms and followers are in Table 2 and Table 3 .

Major themes identified included the following: (1) claiming vaccines were unsafe and/or ineffective, (2) promoting unapproved medications for prevention or treatment, (3) disputing mask-wearing effectiveness, and (4) other misinformation, including unsubstantiated claims, eg, virus origin, government lies, and other conspiracy theories. Supportive quotes are listed in Table 4 .

The most common theme identified was physicians discouraging the public from receiving COVID-19 vaccines. Promoting fear and distrust of the vaccine and reliance on “natural” immunity were common subthemes.

Some of the misinformation propagated by physicians claimed that COVID-19 vaccines were ineffective at preventing COVID-19 spread. A common approach included circulating counts of positive case rates by vaccination status, claiming that most positive cases were among vaccinated individuals. This claim is technically true but misleading, as many more people are vaccinated, and the proportion of unvaccinated people who are infected is much higher. 22 Some stated that the significant increase in case rates after the initial vaccine rollout was evidence for ineffectiveness.

Assertions that COVID-19 vaccines were harmful was not supported by scientific evidence at the time. Unfounded claims included that the vaccines caused infertility, irreparable damage to one’s immune system, increased risk of developing a chronic illness for children, and a higher risk of cancer and death. Claims that myocarditis was common in children who received the vaccine and that the risks of myocarditis outweighed the risk of vaccination were also unfounded. 23 Several physicians redistributed news articles with stories of individuals suddenly or mysteriously dying from the vaccine, despite evidence from the CDC confirming that deaths caused by a COVID vaccine were extremely rare (9 deaths for over 600 million doses administered in the US as of January 2023) and could be attributed only to the Johnson and Johnson COVID-19 vaccine, which was used much less frequently than other manufacturers’ vaccines in many countries. 24

Many of the identified physicians promoted the use of treatments that had not been tested or FDA approved for use in relation to COVID-19. The 2 most prominent medications promoted were ivermectin and hydroxychloroquine, which have been found to not be effective at treating COVID-19 infections in randomized clinical trials. 25 , 26

Anecdotal personal experiences of successfully treating patients with untested medications were commonly used to support claims about safety and effectiveness, such as patients’ conditions were not improving before receiving the untested medication, but the patient recovered after starting the treatment.

Many physicians posted links or screenshots to articles claiming that ivermectin decreased mortality and hospitalization and increased time to recovery and viral clearance. Although some of the articles appeared to be peer-reviewed, none were in high-quality peer-reviewed biomedical journals, and the FDA had not approved the use of these medications for treating COVID-19. At least 1 of the cited articles has been retracted due to misinterpretation of the data. 27

Many of the physicians propagating misinformation about masking effectiveness portrayed masks in a negative light. Claims centered on ineffectiveness, harm, or both.

Most of the misinformation propagated about wearing protective masks asserted that studies conducted before the pandemic definitively concluded that masks do not prevent the spread of respiratory viral infections. Additionally, data showing rising cases in areas enforcing mask mandates were interpreted to mean that the mandates did nothing to slow the spread of infection.

Allegations of consequences of mask wearing included medical and social or developmental effects, all of which were unfounded. 28 Alleged medical consequences included claims that wearing a face mask restricts one’s oxygen, increases the amount of carbon dioxide being inhaled, and causes mask wearers to inhale bacteria that gets trapped. Many physicians focused on negative consequences related to children and mask mandates in schools, claiming that masks interfered with social development despite lack of evidence and that requiring children to wear masks was a form of child abuse.

This misinformation category included conspiracy theories related to domestic and foreign governments and pharmaceutical companies. Theories related to the government included the following: (1) the COVID-19 pandemic was planned by government officials—the “plandemic”; (2) government and public health officials withheld key information regarding COVID-19 from the public, such as hydroxychloroquine effectiveness, falsified statistics to make the virus appear more severe, and censored information that challenged government messaging; (3) the virus originated in a laboratory in China, which contradicted scientific evidence at the time; and (4) the virus was part of a National Institutes of Health–funded study, was leaked, and that the leak was covered up by government and public health officials. Theories related to pharmaceutical companies included that they played a role in discouraging the use of ivermectin and hydroxychloroquine because these medications were inexpensive and easily accessible, and pharmaceutical companies benefited from the promotion of more novel and expensive treatments.

This study was the first, to our knowledge, to identify the types of COVID-19 misinformation propagated by US physicians on social media and the platforms they used, as well as characterize the physicians who spread the misinformation. The content of misinformation physicians spread was similar to the misinformation spread by others; this study contributes new information about the range of specialties and regions of the country the physicians represented. The widely varying number of followers on social media for each physician suggested that the impact of any individual physician’s social media postings also may vary.

Some of the physicians identified belonged to organizations that have been propagating medical misinformation for decades, 10 but these organizations became more vocal and visible in the context of the pandemic’s public health crisis, political divisiveness, and social isolation. Understanding the motivation for misinformation propagation is beyond the scope of this study, but it has become an increasingly profitable industry within and outside of medicine. For example, America’s Frontline Doctors implemented a telemedicine service that charged $90 per consult, primarily to prescribe hydroxychloroquine and ivermectin for COVID-19 to patients across the country, profiting at least $15 million from the endeavor. 29 Twitter’s elimination of safeguards against misinformation 30 and the absence of federal laws regulating medical misinformation on social media platforms suggest that misinformation about COVID-19 and other medical misinformation is likely to persist and may increase. Deregulation of COVID-19 misinformation on social media platforms may have far-reaching implications because consumers may struggle to evaluate the accuracy of the assertions made. 31

National physicians’ organizations, such as the American Medical Association, have called for disciplinary action for physicians propagating COVID-19 misinformation, 32 but stopping physicians from propagating COVID-19 misinformation outside of the patient encounter may be challenging. 33 Although professional speech may be regulated by courts 34 and the FDA has been called on to address medical misinformation, 16 few physicians appear to have faced disciplinary action. Factors such as licensing boards’ lack of resources available to dedicate toward monitoring the internet 35 and state government officials’ challenges to medical boards’ authority to discipline physicians propagating misinformation 36 may limit action.

Scientific evidence depends on a body of accumulated research to inform practice and guidelines and the evidence depends on the best quality research available at any given time. A recent Cochrane Review has been misinterpreted to have definitively shown that wearing masks does not reduce transmission of respiratory viruses and has been used to support assertions that masks definitively “do not work.” 37 Although the Federal Bureau of Investigation and Department of Energy presented a theory to Congress that the COVID-19 virus was the result of a laboratory leak, 38 scientific evidence and a more recent report from the Office of the Director of National Intelligence demonstrate lack of evidence for a laboratory leak and favor a zoonotic origin of the virus. 39 , 40 These recent challenges to prior understandings illuminate the importance of transparency and reproducibility of the process by which conclusions are drawn.

This study had some limitations. We conducted the study in the spring of 2022, after many major social media platforms had begun to establish policies to combat the propagation of COVID-19 misinformation, which means that the current study may underrepresent the extent of misinformation present before these policies were put in place. On some platforms (eg, Twitter), we were unable to analyze all posts by individuals due to the high volume of Tweets and degree of repetition. This study focused on online platforms whose content was readily accessible to the public; different approaches to identifying misinformation and searches of less used platforms might identify other physicians and include other topics. Misinformation disseminated in other ways, such as during clinical care, was not captured. Vaccines had been approved at the start of the period studied, but accessibility may have varied in the early days of the initial rollout. Finally, the state of scientific evidence for COVID-19 guidelines has evolved rapidly over the course of the pandemic, and this study represents a cross-section of time. The current evidence base for preventive and treatment practices, such as duration of vaccine effectiveness, may differ from the evidence base during the study time frame.

Results of this mixed-methods study of the propagation of COVID-19 misinformation by US physicians on social media suggest that physician-propagated misinformation has reached many people during the pandemic and that physicians from a range of specialties and geographic regions have contributed to the “infodemic.” High-quality, ethical health care depends on inviolable trust between health care professionals, their patients, and society. Understanding the degree to which the misinformation about vaccines, medications, masks, and conspiracy theories spread by physicians on social media influences behaviors that put patients at risk for preventable harm, such as illness or death, will help to guide actions to regulate content or discipline physicians who participate in misinformation propagation related to COVID-19 or other conditions. A coordinated response by federal and state governments and the profession that takes free speech carefully into account is needed.

Accepted for Publication: July 6, 2023.

Published: August 15, 2023. doi:10.1001/jamanetworkopen.2023.28928

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Sule S et al. JAMA Network Open .

Correction: This article was corrected on October 25, 2023, to fix dates in several of the quotes in Table 4 due to coding errors and to correct minor wording inaccuracies in several of the quotes. In addition, the date range of the initial social media searches was clarified in the Methods.

Corresponding Author: Sarah L. Goff, MD, PhD, Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts, 715 N Pleasant St, Amherst, MA 01002 ( [email protected] ).

Author Contributions: Dr Goff had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Mss Sule and DaCosta are considered co–first authors.

Concept and design: Sule, Gilson, Goff.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Sule, DaCosta.

Statistical analysis: Sule, DaCosta, Gilson.

Administrative, technical, or material support: DaCosta, DeCou, Gilson, Goff.

Supervision: DaCosta, Goff.

Conflict of Interest Disclosures: Dr Wallace reported contributing to this work while she was a student at University of Massachusetts Amherst, before and outside of her official capacity as a government employee. No other disclosures were reported.

Funding/Support: The study was funded via internal support by the University of Massachusetts (Dr Goff).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed here are those of the authors and do not represent the official policy or position of the US Department of Veteran Affairs or the US government.

Data Sharing Statement: See Supplement 2 .

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• Published: 02 May 2024

Effectiveness of social media-assisted course on learning self-efficacy

• Jiaying Hu 1 ,
• Yicheng Lai 2 &
• Xiuhua Yi 3  

Scientific Reports volume  14 , Article number:  10112 ( 2024 ) Cite this article

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• Human behaviour

The social media platform and the information dissemination revolution have changed the thinking, needs, and methods of students, bringing development opportunities and challenges to higher education. This paper introduces social media into the classroom and uses quantitative analysis to investigate the relation between design college students’ learning self-efficacy and social media for design students, aiming to determine the effectiveness of social media platforms on self-efficacy. This study is conducted on university students in design media courses and is quasi-experimental, using a randomized pre-test and post-test control group design. The study participants are 73 second-year design undergraduates. Independent samples t-tests showed that the network interaction factors of social media had a significant impact on college students learning self-efficacy. The use of social media has a significant positive predictive effect on all dimensions of learning self-efficacy. Our analysis suggests that using the advantages and value of online social platforms, weakening the disadvantages of the network, scientifically using online learning resources, and combining traditional classrooms with the Internet can improve students' learning self-efficacy.

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Introduction.

Social media is a way of sharing information, ideas, and opinions with others one. It can be used to create relationships between people and businesses. Social media has changed the communication way, it’s no longer just about talking face to face but also using a digital platform such as Facebook or Twitter. Today, social media is becoming increasingly popular in everyone's lives, including students and researchers 1 . Social media provides many opportunities for learners to publish their work globally, bringing many benefits to teaching and learning. The publication of students' work online has led to a more positive attitude towards learning and increased achievement and motivation. Other studies report that student online publications or work promote reflection on personal growth and development and provide opportunities for students to imagine more clearly the purpose of their work 2 . In addition, learning environments that include student publications allow students to examine issues differently, create new connections, and ultimately form new entities that can be shared globally 3 , 4 .

Learning self-efficacy is a belief that you can learn something new. It comes from the Latin word “self” and “efficax” which means efficient or effective. Self-efficacy is based on your beliefs about yourself, how capable you are to learn something new, and your ability to use what you have learned in real-life situations. This concept was first introduced by Bandura (1977), who studied the effects of social reinforcement on children’s learning behavior. He found that when children were rewarded for their efforts they would persist longer at tasks that they did not like or had low interest in doing. Social media, a ubiquitous force in today's digital age, has revolutionized the way people interact and share information. With the rise of social media platforms, individuals now have access to a wealth of online resources that can enhance their learning capabilities. This access to information and communication has also reshaped the way students approach their studies, potentially impacting their learning self-efficacy. Understanding the role of social media in shaping students' learning self-efficacy is crucial in providing effective educational strategies that promote healthy learning and development 5 . Unfortunately, the learning curve for the associated metadata base modeling methodologies and their corresponding computer-aided software engineering (CASE) tools have made it difficult for students to grasp. Addressing this learning issue examined the effect of this MLS on the self-efficacy of learning these topics 6 . Bates et al. 7 hypothesize a mediated model in which a set of antecedent variables influenced students’ online learning self-efficacy which, in turn, affected student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments for university courses. Shen et al. 8 through exploratory factor analysis identifies five dimensions of online learning self-efficacy: (a) self-efficacy to complete an online course (b) self-efficacy to interact socially with classmates (c) self-efficacy to handle tools in a Course Management System (CMS) (d) self-efficacy to interact with instructors in an online course, and (e) self-efficacy to interact with classmates for academic purposes. Chiu 9 established a model for analyzing the mediating effect that learning self-efficacy and social self-efficacy have on the relationship between university students’ perceived life stress and smartphone addiction. Kim et al. 10 study was conducted to examine the influence of learning efficacy on nursing students' self-confidence. The objective of Paciello et al. 11 was to identify self-efficacy configurations in different domains (i.e., emotional, social, and self-regulated learning) in a sample of university students using a person-centered approach. The role of university students’ various conceptions of learning in their academic self-efficacy in the domain of physics is initially explored 12 . Kumar et al. 13 investigated factors predicting students’ behavioral intentions towards the continuous use of mobile learning. Other influential work includes 14 .

Many studies have focused on social networking tools such as Facebook and MySpace 15 , 16 . Teachers are concerned that the setup and use of social media apps take up too much of their time, may have plagiarism and privacy issues, and contribute little to actual student learning outcomes; they often consider them redundant or simply not conducive to better learning outcomes 17 . Cao et al. 18 proposed that the central questions in addressing the positive and negative pitfalls of social media on teaching and learning are whether the use of social media in teaching and learning enhances educational effectiveness, and what motivates university teachers to use social media in teaching and learning. Maloney et al. 3 argued that social media can further improve the higher education teaching and learning environment, where students no longer access social media to access course information. Many studies in the past have shown that the use of modern IT in the classroom has increased over the past few years; however, it is still limited mainly to content-driven use, such as accessing course materials, so with the emergence of social media in students’ everyday lives 2 , we need to focus on developing students’ learning self-efficacy so that they can This will enable students to 'turn the tables and learn to learn on their own. Learning self-efficacy is considered an important concept that has a powerful impact on learning outcomes 19 , 20 .

Self-efficacy for learning is vital in teaching students to learn and develop healthily and increasing students' beliefs in the learning process 21 . However, previous studies on social media platforms such as Twitter and Weibo as curriculum support tools have not been further substantiated or analyzed in detail. In addition, the relationship between social media, higher education, and learning self-efficacy has not yet been fully explored by researchers in China. Our research aims to fill this gap in the topic. Our study explored the impact of social media on the learning self-efficacy of Chinese college students. Therefore, it is essential to explore the impact of teachers' use of social media to support teaching and learning on students' learning self-efficacy. Based on educational theory and methodological practice, this study designed a teaching experiment using social media to promote learning self-efficacy by posting an assignment for post-course work on online media to explore the actual impact of social media on university students’ learning self-efficacy. This study examines the impact of a social media-assisted course on university students' learning self-efficacy to explore the positive impact of a social media-assisted course.

Theoretical background

• Social media

Social media has different definitions. Mayfield (2013) first introduced the concept of social media in his book-what is social media? The author summarized the six characteristics of social media: openness, participation, dialogue, communication, interaction, and communication. Mayfield 22 shows that social media is a kind of new media. Its uniqueness is that it can give users great space and freedom to participate in the communication process. Jen (2020) also suggested that the distinguishing feature of social media is that it is “aggregated”. Social media provides users with an interactive service to control their data and information and collaborate and share information 2 . Social media offers opportunities for students to build knowledge and helps them actively create and share information 23 . Millennial students are entering higher education institutions and are accustomed to accessing and using data from the Internet. These individuals go online daily for educational or recreational purposes. Social media is becoming increasingly popular in the lives of everyone, including students and researchers 1 . A previous study has shown that millennials use the Internet as their first source of information and Google as their first choice for finding educational and personal information 24 . Similarly, many institutions encourage teachers to adopt social media applications 25 . Faculty members have also embraced social media applications for personal, professional, and pedagogical purposes 17 .

Social networks allow one to create a personal profile and build various networks that connect him/her to family, friends, and other colleagues. Users use these sites to stay in touch with their friends, make plans, make new friends, or connect with someone online. Therefore, extending this concept, these sites can establish academic connections or promote cooperation and collaboration in higher education classrooms 2 . This study defines social media as an interactive community of users' information sharing and social activities built on the technology of the Internet. Because the concept of social media is broad, its connotations are consistent. Research shows that Meaning and Linking are the two key elements that make up social media existence. Users and individual media outlets generate social media content and use it as a platform to get it out there. Social media distribution is based on social relationships and has a better platform for personal information and relationship management systems. Examples of social media applications include Facebook, Twitter, MySpace, YouTube, Flickr, Skype, Wiki, blogs, Delicious, Second Life, open online course sites, SMS, online games, mobile applications, and more 18 . Ajjan and Hartshorne 2 investigated the intentions of 136 faculty members at a US university to adopt Web 2.0 technologies as tools in their courses. They found that integrating Web 2.0 technologies into the classroom learning environment effectively increased student satisfaction with the course and improved their learning and writing skills. His research focused on improving the perceived usefulness, ease of use, compatibility of Web 2.0 applications, and instructor self-efficacy. The social computing impact of formal education and training and informal learning communities suggested that learning web 2.0 helps users to acquire critical competencies, and promotes technological, pedagogical, and organizational innovation, arguing that social media has a variety of learning content 26 . Users can post digital content online, enabling learners to tap into tacit knowledge while supporting collaboration between learners and teachers. Cao and Hong 27 investigated the antecedents and consequences of social media use in teaching among 249 full-time and part-time faculty members, who reported that the factors for using social media in teaching included personal social media engagement and readiness, external pressures; expected benefits; and perceived risks. The types of Innovators, Early adopters, Early majority, Late majority, Laggards, and objectors. Cao et al. 18 studied the educational effectiveness of 168 teachers' use of social media in university teaching. Their findings suggest that social media use has a positive impact on student learning outcomes and satisfaction. Their research model provides educators with ideas on using social media in the education classroom to improve student performance. Maqableh et al. 28 investigated the use of social networking sites by 366 undergraduate students, and they found that weekly use of social networking sites had a significant impact on student's academic performance and that using social networking sites had a significant impact on improving students' effective time management, and awareness of multitasking. All of the above studies indicate the researcher’s research on social media aids in teaching and learning. All of these studies indicate the positive impact of social media on teaching and learning.

• Learning self-efficacy

For the definition of concepts related to learning self-efficacy, scholars have mainly drawn on the idea proposed by Bandura 29 that defines self-efficacy as “the degree to which people feel confident in their ability to use the skills they possess to perform a task”. Self-efficacy is an assessment of a learner’s confidence in his or her ability to use the skills he or she possesses to complete a learning task and is a subjective judgment and feeling about the individual’s ability to control his or her learning behavior and performance 30 . Liu 31 has defined self-efficacy as the belief’s individuals hold about their motivation to act, cognitive ability, and ability to perform to achieve their goals, showing the individual's evaluation and judgment of their abilities. Zhang (2015) showed that learning efficacy is regarded as the degree of belief and confidence that expresses the success of learning. Yan 32 showed the extent to which learning self-efficacy is viewed as an individual. Pan 33 suggested that learning self-efficacy in an online learning environment is a belief that reflects the learner's ability to succeed in the online learning process. Kang 34 believed that learning self-efficacy is the learner's confidence and belief in his or her ability to complete a learning task. Huang 35 considered self-efficacy as an individual’s self-assessment of his or her ability to complete a particular task or perform a specific behavior and the degree of confidence in one’s ability to achieve a specific goal. Kong 36 defined learning self-efficacy as an individual’s judgment of one’s ability to complete academic tasks.

Based on the above analysis, we found that scholars' focus on learning self-efficacy is on learning behavioral efficacy and learning ability efficacy, so this study divides learning self-efficacy into learning behavioral efficacy and learning ability efficacy for further analysis and research 37 , 38 . Search the CNKI database and ProQuest Dissertations for keywords such as “design students’ learning self-efficacy”, “design classroom self-efficacy”, “design learning self-efficacy”, and other keywords. There are few relevant pieces of literature about design majors. Qiu 39 showed that mobile learning-assisted classroom teaching can control the source of self-efficacy from many aspects, thereby improving students’ sense of learning efficacy and helping middle and lower-level students improve their sense of learning efficacy from all dimensions. Yin and Xu 40 argued that the three elements of the network environment—“learning content”, “learning support”, and “social structure of learning”—all have an impact on university students’ learning self-efficacy. Duo et al. 41 recommend that learning activities based on the mobile network learning community increase the trust between students and the sense of belonging in the learning community, promote mutual communication and collaboration between students, and encourage each other to stimulate their learning motivation. In the context of social media applications, self-efficacy refers to the level of confidence that teachers can successfully use social media applications in the classroom 18 . Researchers have found that self-efficacy is related to social media applications 42 . Students had positive experiences with social media applications through content enhancement, creativity experiences, connectivity enrichment, and collaborative engagement 26 . Students who wish to communicate with their tutors in real-time find social media tools such as web pages, blogs, and virtual interactions very satisfying 27 . Overall, students report their enjoyment of different learning processes through social media applications; simultaneously, they show satisfactory tangible achievement of tangible learning outcomes 18 . According to Bandura's 'triadic interaction theory’, Bian 43 and Shi 44 divided learning self-efficacy into two main elements, basic competence, and control, where basic competence includes the individual's sense of effort, competence, the individual sense of the environment, and the individual's sense of control over behavior. The primary sense of competence includes the individual's Sense of effort, competence, environment, and control over behavior. In this study, learning self-efficacy is divided into Learning behavioral efficacy and Learning ability efficacy. Learning behavioral efficacy includes individuals' sense of effort, environment, and control; learning ability efficacy includes individuals' sense of ability, belief, and interest.

In Fig.  1 , learning self-efficacy includes learning behavior efficacy and learning ability efficacy, in which the learning behavior efficacy is determined by the sense of effort, the sense of environment, the sense of control, and the learning ability efficacy is determined by the sense of ability, sense of belief, sense of interest. “Sense of effort” is the understanding of whether one can study hard. Self-efficacy includes the estimation of self-effort and the ability, adaptability, and creativity shown in a particular situation. One with a strong sense of learning self-efficacy thinks they can study hard and focus on tasks 44 . “Sense of environment” refers to the individual’s feeling of their learning environment and grasp of the environment. The individual is the creator of the environment. A person’s feeling and grasp of the environment reflect the strength of his sense of efficacy to some extent. A person with a shared sense of learning self-efficacy is often dissatisfied with his environment, but he cannot do anything about it. He thinks the environment can only dominate him. A person with a high sense of learning self-efficacy will be more satisfied with his school and think that his teachers like him and are willing to study in school 44 . “Sense of control” is an individual’s sense of control over learning activities and learning behavior. It includes the arrangement of individual learning time, whether they can control themselves from external interference, and so on. A person with a strong sense of self-efficacy will feel that he is the master of action and can control the behavior and results of learning. Such a person actively participates in various learning activities. When he encounters difficulties in learning, he thinks he can find a way to solve them, is not easy to be disturbed by the outside world, and can arrange his own learning time. The opposite is the sense of losing control of learning behavior 44 . “Sense of ability” includes an individual’s perception of their natural abilities, expectations of learning outcomes, and perception of achieving their learning goals. A person with a high sense of learning self-efficacy will believe that he or she is brighter and more capable in all areas of learning; that he or she is more confident in learning in all subjects. In contrast, people with low learning self-efficacy have a sense of powerlessness. They are self-doubters who often feel overwhelmed by their learning and are less confident that they can achieve the appropriate learning goals 44 . “Sense of belief” is when an individual knows why he or she is doing something, knows where he or she is going to learn, and does not think before he or she even does it: What if I fail? These are meaningless, useless questions. A person with a high sense of learning self-efficacy is more robust, less afraid of difficulties, and more likely to reach their learning goals. A person with a shared sense of learning self-efficacy, on the other hand, is always going with the flow and is uncertain about the outcome of their learning, causing them to fall behind. “Sense of interest” is a person's tendency to recognize and study the psychological characteristics of acquiring specific knowledge. It is an internal force that can promote people's knowledge and learning. It refers to a person's positive cognitive tendency and emotional state of learning. A person with a high sense of self-efficacy in learning will continue to concentrate on studying and studying, thereby improving learning. However, one with low learning self-efficacy will have psychology such as not being proactive about learning, lacking passion for learning, and being impatient with learning. The elements of learning self-efficacy can be quantified and detailed in the following Fig.  1 .

Learning self-efficacy research structure in this paper.

Research participants

All the procedures were conducted in adherence to the guidelines and regulations set by the institution. Prior to initiating the study, informed consent was obtained in writing from the participants, and the Institutional Review Board for Behavioral and Human Movement Sciences at Nanning Normal University granted approval for all protocols.

Two parallel classes are pre-selected as experimental subjects in our study, one as the experimental group and one as the control group. Social media assisted classroom teaching to intervene in the experimental group, while the control group did not intervene. When selecting the sample, it is essential to consider, as far as possible, the shortcomings of not using randomization to select or assign the study participants, resulting in unequal experimental and control groups. When selecting the experimental subjects, classes with no significant differences in initial status and external conditions, i.e. groups with homogeneity, should be selected. Our study finally decided to select a total of 44 students from Class 2021 Design 1 and a total of 29 students from Class 2021 Design 2, a total of 74 students from Nanning Normal University, as the experimental subjects. The former served as the experimental group, and the latter served as the control group. 73 questionnaires are distributed to measure before the experiment, and 68 are returned, with a return rate of 93.15%. According to the statistics, there were 8 male students and 34 female students in the experimental group, making a total of 44 students (mirrors the demographic trends within the humanities and arts disciplines from which our sample was drawn); there are 10 male students and 16 female students in the control group, making a total of 26 students, making a total of 68 students in both groups. The sample of those who took the course were mainly sophomores, with a small number of first-year students and juniors, which may be related to the nature of the subject of this course and the course system offered by the university. From the analysis of students' majors, liberal arts students in the experimental group accounted for the majority, science students and art students accounted for a small part. In contrast, the control group had more art students, and liberal arts students and science students were small. In the daily self-study time, the experimental and control groups are 2–3 h. The demographic information of research participants is shown in Table 1 .

Research procedure

Firstly, the ADDIE model is used for the innovative design of the teaching method of the course. The number of students in the experimental group was 44, 8 male and 35 females; the number of students in the control group was 29, 10 male and 19 females. Secondly, the classes are targeted at students and applied. Thirdly, the course for both the experimental and control classes is a convenient and practice-oriented course, with the course title “Graphic Design and Production”, which focuses on learning the graphic design software Photoshop. The course uses different cases to explain in detail the process and techniques used to produce these cases using Photoshop, and incorporates practical experience as well as relevant knowledge in the process, striving to achieve precise and accurate operational steps; at the end of the class, the teacher assigns online assignments to be completed on social media, allowing students to post their edited software tutorials online so that students can master the software functions. The teacher assigns online assignments to be completed on social media at the end of the lesson, allowing students to post their editing software tutorials online so that they can master the software functions and production skills, inspire design inspiration, develop design ideas and improve their design skills, and improve students' learning self-efficacy through group collaboration and online interaction. Fourthly, pre-tests and post-tests are conducted in the experimental and control classes before the experiment. Fifthly, experimental data are collected, analyzed, and summarized.

We use a questionnaire survey to collect data. Self-efficacy is a person’s subjective judgment on whether one can successfully perform a particular achievement. American psychologist Albert Bandura first proposed it. To understand the improvement effect of students’ self-efficacy after the experimental intervention, this work questionnaire was referenced by the author from “Self-efficacy” “General Perceived Self Efficacy Scale” (General Perceived Self Efficacy Scale) German psychologist Schwarzer and Jerusalem (1995) and “Academic Self-Efficacy Questionnaire”, a well-known Chinese scholar Liang 45 .  The questionnaire content is detailed in the supplementary information . A pre-survey of the questionnaire is conducted here. The second-year students of design majors collected 32 questionnaires, eliminated similar questions based on the data, and compiled them into a formal survey scale. The scale consists of 54 items, 4 questions about basic personal information, and 50 questions about learning self-efficacy. The Likert five-point scale is the questionnaire used in this study. The answers are divided into “completely inconsistent", “relatively inconsistent”, “unsure”, and “relatively consistent”. The five options of “Completely Meet” and “Compliant” will count as 1, 2, 3, 4, and 5 points, respectively. Divided into a sense of ability (Q5–Q14), a sense of effort (Q15–Q20), a sense of environment (Q21–Q28), a sense of control (Q29–Q36), a sense of Interest (Q37–Q45), a sense of belief (Q46–Q54). To demonstrate the scientific effectiveness of the experiment, and to further control the influence of confounding factors on the experimental intervention. This article thus sets up a control group as a reference. Through the pre-test and post-test in different periods, comparison of experimental data through pre-and post-tests to illustrate the effects of the intervention.

Reliability indicates the consistency of the results of a measurement scale (See Table 2 ). It consists of intrinsic and extrinsic reliability, of which intrinsic reliability is essential. Using an internal consistency reliability test scale, a Cronbach's alpha coefficient of reliability statistics greater than or equal to 0.9 indicates that the scale has good reliability, 0.8–0.9 indicates good reliability, 7–0.8 items are acceptable. Less than 0.7 means to discard some items in the scale 46 . This study conducted a reliability analysis on the effects of the related 6-dimensional pre-test survey to illustrate the reliability of the questionnaire.

From the Table 2 , the Cronbach alpha coefficients for the pre-test, sense of effort, sense of environment, sense of control, sense of interest, sense of belief, and the total questionnaire, were 0.919, 0.839, 0.848, 0.865, 0.852, 0.889 and 0.958 respectively. The post-test Cronbach alpha coefficients were 0.898, 0.888, 0.886, 0.889, 0.900, 0.893 and 0.970 respectively. The Cronbach alpha coefficients were all greater than 0.8, indicating a high degree of reliability of the measurement data.

The validity, also known as accuracy, reflects how close the measurement result is to the “true value”. Validity includes structure validity, content validity, convergent validity, and discriminative validity. Because the experiment is a small sample study, we cannot do any specific factorization. KMO and Bartlett sphericity test values are an important part of structural validity. Indicator, general validity evaluation (KMO value above 0.9, indicating very good validity; 0.8–0.9, indicating good validity; 0.7–0.8 validity is good; 0.6–0.7 validity is acceptable; 0.5–0.6 means poor validity; below 0.45 means that some items should be abandoned.

Table 3 shows that the KMO values of ability, effort, environment, control, interest, belief, and the total questionnaire are 0.911, 0.812, 0.778, 0.825, 0.779, 0.850, 0.613, and the KMO values of the post-test are respectively. The KMO values are 0.887, 0.775, 0.892, 0.868, 0.862, 0.883, 0.715. KMO values are basically above 0.8, and all are greater than 0.6. This result indicates that the validity is acceptable, the scale has a high degree of reasonableness, and the valid data.

In the graphic design and production (professional design course), we will learn the practical software with cases. After class, we will share knowledge on the self-media platform. We will give face-to-face computer instruction offline from 8:00 to 11:20 every Wednesday morning for 16 weeks. China's top online sharing platform (APP) is Tik Tok, micro-blog (Micro Blog) and Xiao hong shu. The experiment began on September 1, 2022, and conducted the pre-questionnaire survey simultaneously. At the end of the course, on January 6, 2023, the post questionnaire survey was conducted. A total of 74 questionnaires were distributed in this study, recovered 74 questionnaires. After excluding the invalid questionnaires with incomplete filling and wrong answers, 68 valid questionnaires were obtained, with an effective rate of 91%, meeting the test requirements. Then, use the social science analysis software SPSS Statistics 26 to analyze the data: (1) descriptive statistical analysis of the dimensions of learning self-efficacy; (2) Using correlation test to analyze the correlation between learning self-efficacy and the use of social media; (3) This study used a comparative analysis of group differences to detect the influence of learning self-efficacy on various dimensions of social media and design courses. For data processing and analysis, use the spss26 version software and frequency statistics to create statistics on the basic situation of the research object and the basic situation of the use of live broadcast. The reliability scale analysis (internal consistency test) and use Bartlett's sphericity test to illustrate the reliability and validity of the questionnaire and the individual differences between the control group and the experimental group in demographic variables (gender, grade, Major, self-study time per day) are explained by cross-analysis (chi-square test). In the experimental group and the control group, the pre-test, post-test, before-and-after test of the experimental group and the control group adopt independent sample T-test and paired sample T-test to illustrate the effect of the experimental intervention (The significance level of the test is 0.05 two-sided).

Results and discussion

Comparison of pre-test and post-test between groups.

To study whether the data of the experimental group and the control group are significantly different in the pre-test and post-test mean of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. The research for this situation uses an independent sample T-test and an independent sample. The test needs to meet some false parameters, such as normality requirements. Generally passing the normality test index requirements are relatively strict, so it can be relaxed to obey an approximately normal distribution. If there is serious skewness distribution, replace it with the nonparametric test. Variables are required to be continuous variables. The six variables in this study define continuous variables. The variable value information is independent of each other. Therefore, we use the independent sample T-test.

From the Table 4 , a pre-test found that there was no statistically significant difference between the experimental group and the control group at the 0.05 confidence level ( p  > 0.05) for perceptions of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two groups of test groups have the same quality in measuring self-efficacy. The experimental class and the control class are homogeneous groups. Table 5 shows the independent samples t-test for the post-test, used to compare the experimental and control groups on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief.

The experimental and control groups have statistically significant scores ( p  < 0.05) for sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief, and the experimental and control groups have statistically significant scores (t = 3.177, p  = 0.002) for a sense of competence. (t = 3.177, p  = 0.002) at the 0.01 level, with the experimental group scoring significantly higher (3.91 ± 0.51) than the control group (3.43 ± 0.73). The experimental group and the control group showed significance for the perception of effort at the 0.01 confidence level (t = 2.911, p  = 0.005), with the experimental group scoring significantly higher (3.88 ± 0.66) than the control group scoring significantly higher (3.31 ± 0.94). The experimental and control groups show significance at the 0.05 level (t = 2.451, p  = 0.017) for the sense of environment, with the experimental group scoring significantly higher (3.95 ± 0.61) than the control group scoring significantly higher (3.58 ± 0.62). The experimental and control groups showed significance for sense of control at the 0.05 level of significance (t = 2.524, p  = 0.014), and the score for the experimental group (3.76 ± 0.67) would be significantly higher than the score for the control group (3.31 ± 0.78). The experimental and control groups showed significance at the 0.01 level for sense of interest (t = 2.842, p  = 0.006), and the experimental group's score (3.87 ± 0.61) would be significantly higher than the control group's score (3.39 ± 0.77). The experimental and control groups showed significance at the 0.01 level for the sense of belief (t = 3.377, p  = 0.001), and the experimental group would have scored significantly higher (4.04 ± 0.52) than the control group (3.56 ± 0.65). Therefore, we can conclude that the experimental group's post-test significantly affects the mean scores of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. A social media-assisted course has a positive impact on students' self-efficacy.

Comparison of pre-test and post-test of each group

The paired-sample T-test is an extension of the single-sample T-test. The purpose is to explore whether the means of related (paired) groups are significantly different. There are four standard paired designs: (1) Before and after treatment of the same subject Data, (2) Data from two different parts of the same subject, (3) Test results of the same sample with two methods or instruments, 4. Two matched subjects receive two treatments, respectively. This study belongs to the first type, the 6 learning self-efficacy dimensions of the experimental group and the control group is measured before and after different periods.

Paired t-tests is used to analyze whether there is a significant improvement in the learning self-efficacy dimension in the experimental group after the experimental social media-assisted course intervention. In Table 6 , we can see that the six paired data groups showed significant differences ( p  < 0.05) in the pre and post-tests of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. There is a level of significance of 0.01 (t = − 4.540, p  = 0.000 < 0.05) before and after the sense of ability, the score after the sense of ability (3.91 ± 0.51), and the score before the Sense of ability (3.41 ± 0.55). The level of significance between the pre-test and post-test of sense of effort is 0.01 (t = − 4.002, p  = 0.000). The score of the sense of effort post-test (3.88 ± 0.66) will be significantly higher than the average score of the sense of effort pre-test (3.31 ± 0.659). The significance level between the pre-test and post-test Sense of environment is 0.01 (t = − 3.897, p  = 0.000). The average score for post- Sense of environment (3.95 ± 0.61) will be significantly higher than that of sense of environment—the average score of the previous test (3.47 ± 0.44). The average value of a post- sense of control (3.76 ± 0.67) will be significantly higher than the average of the front side of the Sense of control value (3.27 ± 0.52). The sense of interest pre-test and post-test showed a significance level of 0.01 (− 4.765, p  = 0.000), and the average value of Sense of interest post-test was 3.87 ± 0.61. It would be significantly higher than the average value of the Sense of interest (3.25 ± 0.59), the significance between the pre-test and post-test of belief sensing is 0.01 level (t = − 3.939, p  = 0.000). Thus, the average value of a post-sense of belief (4.04 ± 0.52) will be significantly higher than that of a pre-sense of belief Average value (3.58 ± 0.58). After the experimental group’s post-test, the scores for the Sense of ability, effort, environment, control, interest, and belief before the comparison experiment increased significantly. This result has a significant improvement effect. Table 7 shows that the control group did not show any differences in the pre and post-tests using paired t-tests on the dimensions of learning self-efficacy such as sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief ( p  > 0.05). It shows no experimental intervention for the control group, and it does not produce a significant effect.

The purpose of this study aims to explore the impact of social media use on college students' learning self-efficacy, examine the changes in the elements of college students' learning self-efficacy before and after the experiment, and make an empirical study to enrich the theory. This study developed an innovative design for course teaching methods using the ADDIE model. The design process followed a series of model rules of analysis, design, development, implementation, and evaluation, as well as conducted a descriptive statistical analysis of the learning self-efficacy of design undergraduates. Using questionnaires and data analysis, the correlation between the various dimensions of learning self-efficacy is tested. We also examined the correlation between the two factors, and verifies whether there was a causal relationship between the two factors.

Based on prior research and the results of existing practice, a learning self-efficacy is developed for university students and tested its reliability and validity. The scale is used to pre-test the self-efficacy levels of the two subjects before the experiment, and a post-test of the self-efficacy of the two groups is conducted. By measuring and investigating the learning self-efficacy of the study participants before the experiment, this study determined that there was no significant difference between the experimental group and the control group in terms of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two test groups had homogeneity in measuring the dimensionality of learning self-efficacy. During the experiment, this study intervened in social media assignments for the experimental group. The experiment used learning methods such as network assignments, mutual aid communication, mutual evaluation of assignments, and group discussions. After the experiment, the data analysis showed an increase in learning self-efficacy in the experimental group compared to the pre-test. With the test time increased, the learning self-efficacy level of the control group decreased slightly. It shows that social media can promote learning self-efficacy to a certain extent. This conclusion is similar to Cao et al. 18 , who suggested that social media would improve educational outcomes.

We have examined the differences between the experimental and control group post-tests on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. This result proves that a social media-assisted course has a positive impact on students' learning self-efficacy. Compared with the control group, students in the experimental group had a higher interest in their major. They showed that they liked to share their learning experiences and solve difficulties in their studies after class. They had higher motivation and self-directed learning ability after class than students in the control group. In terms of a sense of environment, students in the experimental group were more willing to share their learning with others, speak boldly, and participate in the environment than students in the control group.

The experimental results of this study showed that the experimental group showed significant improvement in the learning self-efficacy dimensions after the experimental intervention in the social media-assisted classroom, with significant increases in the sense of ability, sense of effort, sense of environment, sense of control, sense of interest and sense of belief compared to the pre-experimental scores. This result had a significant improvement effect. Evidence that a social media-assisted course has a positive impact on students' learning self-efficacy. Most of the students recognized the impact of social media on their learning self-efficacy, such as encouragement from peers, help from teachers, attention from online friends, and recognition of their achievements, so that they can gain a sense of achievement that they do not have in the classroom, which stimulates their positive perception of learning and is more conducive to the awakening of positive effects. This phenomenon is in line with Ajjan and Hartshorne 2 . They argue that social media provides many opportunities for learners to publish their work globally, which brings many benefits to teaching and learning. The publication of students' works online led to similar positive attitudes towards learning and improved grades and motivation. This study also found that students in the experimental group in the post-test controlled their behavior, became more interested in learning, became more purposeful, had more faith in their learning abilities, and believed that their efforts would be rewarded. This result is also in line with Ajjan and Hartshorne's (2008) indication that integrating Web 2.0 technologies into classroom learning environments can effectively increase students' satisfaction with the course and improve their learning and writing skills.

We only selected students from one university to conduct a survey, and the survey subjects were self-selected. Therefore, the external validity and generalizability of our study may be limited. Despite the limitations, we believe this study has important implications for researchers and educators. The use of social media is the focus of many studies that aim to assess the impact and potential of social media in learning and teaching environments. We hope that this study will help lay the groundwork for future research on the outcomes of social media utilization. In addition, future research should further examine university support in encouraging teachers to begin using social media and university classrooms in supporting social media (supplementary file 1 ).

The present study has provided preliminary evidence on the positive association between social media integration in education and increased learning self-efficacy among college students. However, several avenues for future research can be identified to extend our understanding of this relationship.

Firstly, replication studies with larger and more diverse samples are needed to validate our findings across different educational contexts and cultural backgrounds. This would enhance the generalizability of our results and provide a more robust foundation for the use of social media in teaching. Secondly, longitudinal investigations should be conducted to explore the sustained effects of social media use on learning self-efficacy. Such studies would offer insights into how the observed benefits evolve over time and whether they lead to improved academic performance or other relevant outcomes. Furthermore, future research should consider the exploration of potential moderators such as individual differences in students' learning styles, prior social media experience, and psychological factors that may influence the effectiveness of social media in education. Additionally, as social media platforms continue to evolve rapidly, it is crucial to assess the impact of emerging features and trends on learning self-efficacy. This includes an examination of advanced tools like virtual reality, augmented reality, and artificial intelligence that are increasingly being integrated into social media environments. Lastly, there is a need for research exploring the development and evaluation of instructional models that effectively combine traditional teaching methods with innovative uses of social media. This could guide educators in designing courses that maximize the benefits of social media while minimizing potential drawbacks.

In conclusion, the current study marks an important step in recognizing the potential of social media as an educational tool. Through continued research, we can further unpack the mechanisms by which social media can enhance learning self-efficacy and inform the development of effective educational strategies in the digital age.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

This work is supported by the 2023 Guangxi University Young and middle-aged Teachers' Basic Research Ability Enhancement Project—“Research on Innovative Communication Strategies and Effects of Zhuang Traditional Crafts from the Perspective of the Metaverse” (Grant Nos. 2023KY0385), and the special project on innovation and entrepreneurship education in universities under the “14th Five-Year Plan” for Guangxi Education Science in 2023, titled “One Core, Two Directions, Three Integrations - Strategy and Practical Research on Innovation and Entrepreneurship Education in Local Universities” (Grant Nos. 2023ZJY1955), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform General Project (Category B) “Research on the Construction and Development of PBL Teaching Model in Advertising” (Grant Nos.2023JGB294), and the 2022 Guangxi Higher Education Undergraduate Teaching Reform Project (General Category A) “Exploration and Practical Research on Public Art Design Courses in Colleges and Universities under Great Aesthetic Education” (Grant Nos. 2022JGA251), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform Project Key Project “Research and Practice on the Training of Interdisciplinary Composite Talents in Design Majors Based on the Concept of Specialization and Integration—Taking Guangxi Institute of Traditional Crafts as an Example” (Grant Nos. 2023JGZ147), and the2024 Nanning Normal University Undergraduate Teaching Reform Project “Research and Practice on the Application of “Guangxi Intangible Cultural Heritage” in Packaging Design Courses from the Ideological and Political Perspective of the Curriculum” (Grant Nos. 2024JGX048),and the 2023 Hubei Normal University Teacher Teaching Reform Research Project (Key Project) -Curriculum Development for Improving Pre-service Music Teachers' Teaching Design Capabilities from the Perspective of OBE (Grant Nos. 2023014), and the 2023 Guangxi Education Science “14th Five-Year Plan” special project: “Specialized Integration” Model and Practice of Art and Design Majors in Colleges and Universities in Ethnic Areas Based on the OBE Concept (Grant Nos. 2023ZJY1805), and the 2024 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project “Research on the Integration Path of University Entrepreneurship and Intangible Inheritance - Taking Liu Sanjie IP as an Example” (Grant Nos. 2024KY0374), and the 2022 Research Project on the Theory and Practice of Ideological and Political Education for College Students in Guangxi - “Party Building + Red”: Practice and Research on the Innovation of Education Model in College Student Dormitories (Grant Nos. 2022SZ028), and the 2021 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project - "Research on the Application of Ethnic Elements in the Visual Design of Live Broadcast Delivery of Guangxi Local Products" (Grant Nos. 2021KY0891).

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The contribution of H. to this paper primarily lies in research design and experimental execution. H. was responsible for the overall framework design of the paper, setting research objectives and methods, and actively participating in data collection and analysis during the experimentation process. Furthermore, H. was also responsible for conducting literature reviews and played a crucial role in the writing and editing phases of the paper. L.'s contribution to this paper primarily manifests in theoretical derivation and the discussion section. Additionally, author L. also proposed future research directions and recommendations in the discussion section, aiming to facilitate further research explorations. Y.'s contribution to this paper is mainly reflected in data analysis and result interpretation. Y. was responsible for statistically analyzing the experimental data and employing relevant analytical tools and techniques to interpret and elucidate the data results.

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Hu, J., Lai, Y. & Yi, X. Effectiveness of social media-assisted course on learning self-efficacy. Sci Rep 14 , 10112 (2024). https://doi.org/10.1038/s41598-024-60724-0

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Article Contents

Lay summary, theoretical background and related work, the current study, summary and discussion, data availability, active social media use and its impact on well-being — an experimental study on the effects of posting pictures on instagram.

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Hannes-Vincent Krause, Fenne große Deters, Annika Baumann, Hanna Krasnova, Active social media use and its impact on well-being — an experimental study on the effects of posting pictures on Instagram, Journal of Computer-Mediated Communication , Volume 28, Issue 1, January 2023, zmac037, https://doi.org/10.1093/jcmc/zmac037

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Active use of social networking sites (SNSs) has long been assumed to benefit users’ well-being. However, this established hypothesis is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with considerable heterogeneity among existing studies on the hypothesis and causal evidence still limited, a final verdict on its robustness is still pending. To contribute to this ongoing debate, we conducted a week-long randomized control trial with N = 381 adult Instagram users recruited via Prolific. Specifically, we tested how active SNS use, operationalized as picture postings on Instagram, affects different dimensions of well-being. The results depicted a positive effect on users’ positive affect but null findings for other well-being outcomes. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs.

Active use of social networking sites (SNSs) has long been assumed to benefit users’ well-being. However, this established assumption is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with great diversity among conducted studies on the hypothesis and a lack of causal evidence, a final verdict on its viability is still pending. To contribute to this ongoing debate, we conducted a week-long experimental investigation with 381 adult Instagram users. Specifically, we tested how posting pictures on Instagram affects different aspects of well-being. The results of this study depicted a positive effect of posting Instagram pictures on users’ experienced positive emotions but no effects on other aspects of well-being. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs on users.

An extensive body of research has been devoted to whether and how using social networking sites (SNSs) affects well-being. So far, the research discourse has been heavily influenced by the active social media use hypothesis (ASUH). The ASUH posits that active SNS use represents a beneficial type of SNS participation, especially compared to presumably well-being-threatening passive usage ( Burke et al., 2010 ; Verduyn et al., 2017 ). Active SNS use comprises SNS activities that facilitate social interactions ( Verduyn et al., 2015 ) — whether through content sharing or other types of social exchange. The ASUH assumes positive well-being consequences for this usage type, such as fostering social connections (e.g., Ellison et al., 2014 ), overcoming perceptions of loneliness (e.g., große Deters & Mehl, 2012 ), or promoting users’ self-esteem (e.g., Krause et al., 2021 ).

Theoretically, the assumed positive effect of active SNS use can be traced back to two mechanisms. First, the information provided within active SNS use can be a starting point for social interactions, potentially fostering well-being-promoting social outcomes ( Verduyn et al., 2017 ). Second, active use can promote well-being as it requires a certain level of self-reflection, potentially shifting users’ attention to positive aspects of their selves or enabling self-affirmation ( Vogel & Rose, 2016 ).

So far, evidence for the ASUH and the overall distinction between active and passive use (henceforth referred to as active/passive dichotomy; e.g., Meier & Krause, 2022 ) has been mainly consolidated by several narrative reviews (e.g., Ellison et al., 2014 ; Lin et al., 2020 ; Verduyn et al., 2017 ; Vogel & Rose, 2016 ) that aimed to explain the potpourri of previous findings on SNSs’ effects on users’ well-being (for umbrella reviews, see Appel et al., 2020 ; Valkenburg, 2022 ; Valkenburg et al., 2022a ). However, recently the ASUH has faced critique. Specifically, scholars point to the lacking conceptual precision of the active/passive dichotomy, which seems to divide complex SNS actions into artificial categories, too coarse to accurately reflect users’ experience on SNSs ( Meier & Krause, 2022 ; Valkenburg et al., 2022b ). Most strikingly, the ASUH appears to lack a solid empirical foundation ( Valkenburg, 2022 ; Valkenburg et al., 2022b ). For instance, a recent study showed that the hypothesized positive effects of active use could only be confirmed for a small proportion of users ( Beyens et al., 2021 ). Similarly, the effects of active SNS use on numerous well-being outcomes have been found to be minor in a meta-analysis ( Liu et al., 2019 ) or insignificant in a recent scoping review ( Valkenburg et al., 2022b ). Especially since null findings often remain in the “file drawer” and meta-analyses might therefore overestimate effects ( Ferguson & Heene, 2012 ; Rosenthal, 1979 ), the emerging evidence increasingly challenges the robustness of the ASUH.

Nonetheless, these scoping reviews and meta-analyses point to a substantial degree of heterogeneity in the included studies, making it difficult to aggregate the results and make a definitive judgment about the viability of the ASUH ( Valkenburg et al., 2022b ). Three limitations of previous research on the ASUH are especially critical and deserve detailed explanation:

A major shortcoming of the existing literature is the ambiguity in conceptualizing and operationalizing active SNS use ( Trifiro & Gerson, 2019 ; Valkenburg et al., 2022b ). Active use covers multiple and diverse actions, but most research has not comprehensively accounted for these differences ( Trifiro & Gerson, 2019 ). Instead, diverse operationalizations of active SNS use have been applied, rarely covering the same actions ( Trifiro & Gerson, 2019 ; Valkenburg et al., 2022b ). Because different SNS actions are likely driven by different user motivations and needs ( Yang et al., 2021 ), a valid synthesis into a single construct to test the ASUH seems questionable ( Kross et al., 2021 ). Following calls on general SNS research (e.g., Meier & Reinecke, 2021 ), we propose to focus on concrete and conceptually clear active use actions in testing the ASUH. This granular approach might not allow generalizing findings across other active use actions. Still, it enables isolating effects and combats the risk that inherently different actions synthesized into one construct bias effect estimates and thereby overall claims about the validity of the ASUH. Similarly, critique has been raised against research’s almost exclusive use of self-reported SNS usage data because self-reports are bias-prone (e.g., Parry et al., 2021 ). Self-reports are certainly warranted whenever the perception of one’s own SNS usage is central to the respective research question (e.g., Ernala et al., 2022 ). However, to test the ASUH, the main aim is to capture participants’ actual behaviors. Numerous scholars encourage complementing existing self-report-based findings with more objective methods (e.g., server logs; built-in tracking tools) ( Kross et al., 2021 ; Meier & Reinecke, 2021 ; Valkenburg, 2022 ).

Research investigating the ASUH showed vast diversity in conceptualizing and operationalizing their central outcome: well-being. Subjective well-being is defined as a dyad of satisfaction with one’s life and a balance of positive and negative affect ( Diener, 1984 ). A recent meta-review ( Meier & Reinecke, 2021 ) comprehensively outlines the heterogeneity of existing research on the effects of SNS use in operationalizing well-being. For example, some work has focused on the effects of active SNS use on constructs that can benefit or promote well-being (i.e., resilience factors; Meier & Reinecke, 2021 ), whereas other scholars focused on constructs than can threaten well-being (i.e., risk factors; Meier & Reinecke, 2021 ). Others directly captured the core dimensions of subjective well-being (i.e., affect and life satisfaction) in their applied operationalization (e.g., Kim et al., 2014 ; Yang, 2020 ). Globally, these diverse approaches impede a conclusive picture of how active SNS use affects well-being and leave open whether it contributes exclusively to reducing risk factors, promoting resilience factors, influences well-being as a whole or is unrelated to any of these factors ( Meier & Reinecke, 2021 ; Valkenburg, 2022 ).

However, one of the most severe limitations of the existing literature on the ASUH (e.g., Kross et al., 2021 ; Verduyn et al., 2017 ) is the overuse of cross-sectional and correlational research designs. This severely complicates conclusions about causality. To the best of our knowledge, only 10 experimental studies manipulated active use or any of the associated actions and examined their impact on well-being or its risk and resilience factors. Like cross-sectional studies, existing experimental studies draw an ambiguous picture. Approximately half of these experiments reported positive effects of active use on well-being in line with the ASUH ( große Deters & Mehl, 2012 ; Hanley et al., 2019 ; Pit et al., 2022 ; Roberts & David, 2022 ; Tobin et al., 2014 ). The other half reported null ( Verduyn et al., 2015 ; Yuen et al., 2018 ) or mixed findings. For instance, positive effects were only reported for specific users ( Hunt et al., 2021 ) or for posting inspiring vs. hedonic content ( Janicke-Bowles et al., 2022 ). One study could only detect a short-term effect on specific appearance-related outcomes ( Coulthard & Ogden, 2018 ). However, these experiments again unite a considerable degree of conceptual and methodological heterogeneity. Most studies applied a mixed operationalization of active use. These often instructed participants to engage actively on SNS for a defined period in line with the original active use definition ( Verduyn et al., 2015 ). Few studies manipulated specific actions, such as Facebook status updates ( große Deters & Mehl, 2012 ), selfies ( Coulthard & Ogden, 2018 ), or other specifics of the content (e.g., if it is inspiring vs. hedonic; Janicke-Bowles et al., 2022 ). Moreover, studies varied concerning the considered outcomes, either exclusively looking at core aspects of well-being (e.g., Hanley et al., 2019 ) or at specific risk or resilience factors (e.g., loneliness; große Deters & Mehl, 2012 ). In addition, most studies investigated the SNS Facebook and only rarely applied objective manipulation checks.

To address the outlined shortcomings of previous literature, the current study investigated the causal effects of active SNS use on well-being using a randomized control trial (RCT). Specifically, we tested in an experiment how increasing the frequency of posting pictures on Instagram over one week impacts users’ subjective well-being as well as its risk and resilience factors.

The experimental nature of our study contributes to the rare and much-needed causal evidence ( Kross et al., 2021 ) on the ASUH. The focus on one specific active SNS usage activity (i.e., posting pictures) avoids the vagueness of other active operationalizations and allowed us to fathom effects that can be attributed to this activity alone. Additionally, we used participants’ objective Instagram usage data as a manipulation check, thereby avoiding the caveats of self-reported usage data (e.g., Parry et al., 2021 ). Finally, we considered both the central aspects (i.e., affect and life satisfaction) of well-being and its risk and resilience factors (i.e., self-esteem and loneliness) to attend to its multifaceted nature ( Meier & Reinecke, 2021 ). This allowed us to determine which aspects are affected by active use, either in a beneficial, harmful, or no way at all.

Active social media use

The ASUH and the active/passive SNS use dichotomy build on the assumption that multiple SNS actions fall into two qualitatively different categories. Initially labeled as direct communication vs. consumption ( Burke et al., 2010 ), this dichotomization eventually led to the established active/passive use dichotomy ( Verduyn et al., 2017 ).

Active SNS use constitutes a heterogeneous construct subsuming different actions ( Valkenburg et al., 2022b ). The core element of active use is the facilitation of social interactions between users ( Valkenburg et al., 2022b ; Verduyn et al., 2017 , Verduyn et al., 2022 ). However, the exact activities accounting for this usage type are yet not commonly defined and span from posting or resharing content to sending connection requests or exchanging private direct messages between users ( Valkenburg et al., 2022b ). Active SNS use covers such a broad spectrum of actions (see Figure 1 ) that it was suggested to differentiate them further based on their level of publicness or interactivity, including private or interactive active use actions (e.g., sending direct messages) and public active use actions (e.g., public posting of content) ( Valkenburg et al., 2022b ; Yang et al., 2021 ). These actions differ in quality and their frequency of occurrence, additionally challenging a valid synthesis into a global construct of active use ( Faelens et al., 2019 ; Valkenburg et al., 2022b ). Moreover, there has been concern over the ambiguity of specific actions (e.g., liking and commenting) seemingly falling into the spectrum of both active and passive use ( Ellison et al., 2020 ; Valkenburg et al., 2022b ).

Overview of a selection of different SNS actions and their assignment to the proposed public and private active SNS use categories ( Valkenburg et al., 2022b ). The dashed area illustrates ambiguity in the assignment of certain actions to the active/passive dichotomy.

The well-being benefiting effect of active SNS use

The active/passive dichotomy attributes unique social psychological processes to each of the two usage types ( Verduyn et al., 2017 , Verduyn et al., 2022 ; Vogel & Rose, 2016 ), explaining their hypothesized differential effects on well-being.

While passive use’s assumed adverse effect is primarily grounded in SNSs’ potential to elicit upward social comparisons ( Festinger, 1954 ; Verduyn et al., 2020 ), the theoretical grounds of the ASUH are more diverse. So far, researchers have mainly relied on two different dimensions of active SNS use (see Figure 2 ) to explain its well-being-benefitting effects: the social ( Burke et al., 2011 ; Verduyn et al., 2017 ) and the self-dimension ( Vogel & Rose, 2016 ).

Theoretical framework depicting the two routes under which active SNS use can impact well-being.

The social dimension of active SNS use

Social connectedness is a universal need and a strong determinant of individuals’ well-being ( Baumeister & Leary, 1995 ; Ryan & Deci, 2000 ). Active use of SNSs can fulfill this need as it constitutes a fundamentally social act. By sharing information on SNSs, users inevitably reveal something about themselves while simultaneously opening a venue for social discourse, offering others to engage with their digital representations. Indeed, SNSs have been shown to facilitate social feedback in multiple ways ( Krause et al., 2021 ). For example, low-threshold functionalities such as liking or commenting allow others to express their support and engage in a social dialog effortlessly and rapidly ( Barasch & Berger, 2014 ; Oh et al., 2014 ). By allowing likes, comments, or reshares to one’s shared self-presentation, active use of SNSs can directly contribute to the satisfaction of users’ need to be liked, accepted, and approved by others ( Leary, 1999 ), which may ultimately positively impact users’ self-view or self-esteem (for a review, see Krause et al., 2021 ). Similarly, research stresses active use’s alleviating effects on users’ perceived loneliness ( Burke et al., 2010 ; große Deters & Mehl, 2012 ; Lin et al., 2020 ; Matook et al., 2015 ). Further, active use can strengthen existing or create new social connections and promote a sense of social connectedness, supporting the accumulation of social capital (e.g., Burke et al., 2010 ; Ellison et al., 2014 ).

In summary, active use lays the groundwork for social interactions, potentially satisfying users’ connectedness needs and facilitating the build-up of social capital. With social connections as a critical determinant of human well-being (for a review, see Diener et al., 2018 ), the social dimension offers one potential route to explain the benefitting effects of active use, as posited by the ASUH.

The self-dimension of active SNS use

Active SNS use can also involve psychological processes more directed toward users’ self ( Vogel & Rose, 2016 ). This type of use inevitably requires a certain degree of self-reflection and can be seen as a form of self-presentation ( Vogel & Rose, 2016 ). As part of social impression management, individuals engage in self-presentation in almost all aspects of daily social life ( Baumeister, 1982 ). SNSs offer users an effective venue to satisfy this need for self-presentation, which can benefit users’ well-being ( Kim & Lee, 2011 ). In addition, acts of self-presentation on SNSs also exhibit a unique quality. Well-established platform functionalities such as content-editing tools (e.g., static photo filters, real-time video filters) and the asynchronous nature of computer-mediated communication allow one to carefully construe and present the desired self-impression to others ( Ellison et al., 2006 ; Qiu et al., 2012 ). This is further supported by the norm of positive self-presentation established on SNSs, which normalizes positive, idealized, and socially desirable self-presentation (e.g., Brunskill, 2013 ; Harris & Bardey, 2019 ; Reinecke & Trepte, 2014 ).

The implicit focus on positive aspects of the self, inherent in active SNS use, is ascribed a vital role in the ASUH ( Vogel & Rose, 2016 ). First, presenting a polished and desirable image of one’s life or self on SNSs could get integrated into one’s self-concept, benefiting how users see and think about themselves ( Gonzales & Hancock, 2011 ; Vogel & Rose, 2016 ; Yang & Bradford Brown, 2016 ). Second, even the mere reflection on positive aspects of the self can be conducive. Self-affirmation (for a review, see McQueen & Klein, 2006 ) has been long-discussed as a non-negligible aspect of active SNS use ( Toma, 2013 ; Toma & Hancock, 2013 ). Shifting attention to positive aspects of oneself — such as when looking at or editing one’s SNS profile — can mitigate the effects of self-threatening information ( Toma & Hancock, 2013 ) and boost self-esteem ( Toma, 2013 ).

To summarize, active SNS use can benefit users’ well-being in multiple ways. Theoretically, the social dimension of content sharing — especially in terms of enabling social interactions — could satisfy users’ social connectedness needs and contribute to growing social capital. In contrast, the self-dimension of active SNS use — as evident in the self-reflection surrounding active SNS use actions — could satisfy users’ self-presentation needs, benefit their self-perception and self-esteem, and have self-affirming qualities.

The solid theoretical rationale of ASUH seems to be at odds with its lack of empirical foundation (e.g., Valkenburg et al., 2022b ). Still, previous research on the ASUH exhibits several limitations and a lack of causal evidence, complicating a definitive judgment on the viability of ASUH. Therefore, this study aims to contribute to the ongoing debate on the ASUH, avoiding the caveats of previous research and providing further empirical clarity on the effects of active use.

We conducted an online RCT study using a sample of British adult Instagram users. We tested how an increase in posting pictures on their own Instagram profile over one week impacts different facets of their well-being. We operationalized active use as one concrete and quantifiable action: posting Instagram pictures. Posting pictures is a recurring component in numerous operationalizations of active use (e.g., Jarman et al., 2021 ; Marengo et al., 2021 ; Nisar et al., 2019 ) and, unlike other activities (e.g., liking and commenting; Ellison et al., 2020 ), can be assigned to the active use complex without objection. Posting pictures is an elementary action for most SNSs and is especially crucial for the mostly picture-based platform Instagram. We further assume that posting Instagram pictures most strongly taps into the two proposed theoretical mechanisms underlying active use and is, therefore, the most suitable for testing the ASUH.

Regarding the outcome, we considered multiple well-being aspects that have gained attention in the context of SNS use ( Valkenburg et al., 2022b ). We included core facets of subjective well-being: life satisfaction and affect ( Diener, 1984 ), as well as resilience and risk factors such as loneliness and self-esteem ( Meier & Reinecke, 2021 ). We further captured participants’ perceptions of social connectedness and self-affirmation to test for the two theoretically assumed dimensions underlying the well-being profiting effects of active SNS use (i.e., the social and the self-dimension).

Sample and design

The data collection took place in April 2021. The final sample consisted of N  =   381 British Instagram users recruited via the online platform Prolific. Participants were pre-screened based on their country of residence, nationality (both UK), and whether they use Instagram regularly. All participants received monetary compensation of 7.00£ for full participation in all included questionnaires. Participants’ age ranged from 18 to 61 years ( M age = 32.5, SD age = 9.6), and 79.8% were female. Most participants (40%) reported an undergraduate degree, 24% A-levels, and 16% a graduate degree as their highest level of education. Only 23% of the sample were students. Around 53% of the sample reported being in full-time employment (20% part-time, 10% not in paid work, 9% unemployed). The target sample size was determined based on the effect sizes reported by Liu et al. (2019) and große Deters and Mehl (2012) , which indicated a small effect ( Cohen, 1988 ) of active SNS use on well-being. A conducted power analysis (G*Power 3.1, Erdfelder et al., 1996 ) suggested a sample size of N  =   351 to be able to detect the targeted small effect of active SNS use (i.e., partial η 2  = 0.022, Cohen, 1988 ) with a power of 80% (α = 0.05).

The online study was designed as an RCT with a pretest/posttest control group design to investigate the causal impact of active Instagram use on well-being over one week. The design included one experimental group (EG) and a control group (CG). The study consisted of a baseline assessment (T1) on the first day of the study, a post-assessment (T2) on the seventh day after the baseline measure, and six shorter daily questionnaires for the days in-between T1 and T2. Subjects were randomly allocated into EG and CG using a stratified randomization procedure. 1 To manipulate active Instagram use as a between-subject factor, participants were instructed to post more pictures on their own Instagram profile than usually throughout the study week (EG) or continue using Instagram as usual (CG).

Procedure and measures

All questionnaires were hosted on the online survey platform SoSci Survey ( Leiner, 2019 ). After registering for the study and providing informed consent, participants completed the baseline questionnaire (T1), including demographics, measures about their Instagram use (e.g., average time spent on Instagram, number of followers), and the baseline outcome measures (i.e., life satisfaction, positive/negative affect, self-esteem, and loneliness).

Life satisfaction was assessed using the Satisfaction with Life Scale ( Diener et al., 1985 ), which consists of five items (e.g., I am satisfied with my life. ) and assesses participants’ general agreement on a seven-point Likert scale (1 =  strongly disagree ; 7 =  strongly agree, Cronbach’s α  = 0.90, M  =   4.34, SD =  1.39). Participants’ positive (10 items; e.g., active, interested; Cronbach’s α  = 0.91, M  =   2.54, SD =  0.78) and negative affect (10 items; e.g., distressed, upset; Cronbach’s α  = 0.92, M  =   1.48, SD =  0.62) were captured with the Positive and Negative Affect Schedule ( Watson et al., 1988 ). Participants had to describe their feelings at this moment using a five-point rating scale (1 =  not at all ; 5 =  extremely ). Levels of perceived loneliness were measured using the University of California, Los Angeles loneliness scale ( Russell, 1996 ). On a four-point rating scale (1= never ; 4 =  often , Cronbach’s α  = 0.95, M  =   2.25, SD =  0.61), participants indicated how often each of the 20 items (e.g., How often do you feel left out? ) is descriptive of them. We administered Rosenberg’s Self-Esteem Scale ( Rosenberg, 1965 ) to measure trait self-esteem consisting of 10 items (e.g., I take a positive attitude toward myself. ; Cronbach’s α  = 0.92, M  =   2.77, SD =  0.63) answered on a four-point Likert scale (1 =  strongly agree ; 4 =  strongly disagree ).

For our experimental manipulation, we randomly assigned participants to either EG or CG. In the first step, we asked participants how many pictures they usually post on their Instagram profile per week. 82% of participants reported usually posting less than one picture. The rest indicated usually posting M  =   2.9 pictures per week ( SD  =   2.0). Upon the end of T1, we reminded participants about their previously specified number of usual postings and presented the instruction according to their respective condition. Participants in the EG were instructed to post more pictures on Instagram during the study than the specified number. In contrast, participants in the CG were instructed to continue using Instagram as they usually would.

Participants filled out six short daily questionnaires on the days in-between T1 and T2. These daily questionnaires included items about the number of posted pictures and stories since the completion of the last questionnaire and an item about their current level of perceived social connectedness (At the moment, I feel connected and in touch with my friends ; five-point Likert-scale: 1 =  strongly disagree ; 5 =  strongly agree; averaged across all daily questionnaires : M  =   3.43, SD =  0.89). 2 Participants were reminded about their instructions for the course of the study at the end of each daily questionnaire.

The study ended with the T2 questionnaire one week after the completion of T1. Besides the same outcome measures as at T1 (i.e., life satisfaction, positive/negative affect, self-esteem, and loneliness), T2 included a scale measuring participants’ perception of self-affirmation throughout the study ( Napper et al., 2009 ). The scale consists of five items, slightly adapted for our study (e.g., The eight days of this study made me aware of things I value about myself.; Cronbach’s α  = 0.91, M  =   3.41, SD =  0.86) and answered on a five-point Likert scale (1 =  strongly disagree ; 5 =  strongly agree ).

As a manipulation check, we asked participants at T1 and T2 to upload anonymized screenshots of the upper part of their Instagram profile, displaying the total number of posts added to the profile to this date. Following the call for more objective and reliable methods to capture SNS usage ( Kross et al., 2021 ), we used the difference between these numbers at T1 and T2 to verify that participants in the EG followed the given instruction (i.e., posted more pictures than usual).

Dropout analyses

Initially, N  =   403 participants completed T1 and were added to the panel list of the study. Of those, N  =   391 participants completed T2. Ten participants were removed due to failed attention checks in both T1 and T2 or for completing T2 more than 8 days after the completion of T1, leading to a final sample of N  =   381 participants (dropout from T1 to T2 = 5.5%).

For the full sample, dropouts did not statistically differ from the remaining participants in any of the collected demographics, the outcomes at baseline, and their usual posting frequency. More importantly, there was no systematic difference with respect to dropouts’ baseline outcome measures between EG and CG, and hence, the data did not show any signs of selective attrition bias. 3

Manipulation check and compliance

To ensure the manipulation’s success, we assessed the number of pictures posted during the study based on the uploaded screenshots from participants’ Instagram profiles. For cases where screenshots were missing or clearly faulty (4% of the sample), the self-reported number of pictures posted during the study was used instead. The full sample posted M =  1.83 ( SD  =   2.95) pictures during the study, thereby M difference = 1.31 pictures more than usual ( SD difference = 2.55). Participants in the EG ( N  =   183) showed a strong increase ( d  =   0.76) in their posting frequency and posted M difference = 2.19 pictures more than usual ( M baseline = 0.42, SD baseline = 1.21) during the study ( SD difference = 2.87, t [182] = 10.31, p < .001). Participants in the CG ( N  =   198) also registered a small increase ( d  =   0.26) and posted M difference = 0.49 pictures more than usual ( M baseline = 0.62, SD baseline = 1.56) during the study ( SD difference = 1.88, t [197] = 3.71, p < .001). EG and CG significantly differed in their posting frequency increase (Welch t test, t [309.5] = 6.76, p < .001), indicating a successful manipulation.

In the EG, 31% of participants did not comply with the given instruction (i.e., posting the same number or fewer pictures than usual). A similar share in the CG (34%) showed non-compliance (i.e., posting more or fewer pictures during the study compared to their usual frequency). We opted for more conservative testing and followed an intention-to-treat approach ( Sagarin et al., 2014 ). That is, for the analyses, no participants were excluded from the final sample, and all participants were kept in their originally assigned condition, regardless of their compliance with the given instruction. Therefore, the EG included both participants that increased their usual posting frequency or did not show a change in their usual posting frequency (or posted even less). The CG included both participants that did not change their usual frequency or did so (i.e., posting more or less than usual). Essentially, this ensures that randomization is maintained and allows to fathom unbiased effect estimates ( Sagarin et al., 2014 ).

Effect of active Instagram use

Linear regression models were calculated to analyze the effects of active SNS use. Each respective well-being outcome at T2 was regressed on its level at T1 and condition (dummy coded: 0 = CG; 1 = EG), which corresponds to an analysis of covariance (ANCOVA) and is the recommended approach to analyze data from a randomized pretest/post-test control group design (e.g., van Breukelen, 2006 ). A different methodological approach — but with less power — is to test for differences in average change between the conditions (CHANGE, van Breukelen, 2013 ). The two approaches can sometimes yield different results (Lord’s paradox, van Breukelen, 2006 ). For all outcomes, we additionally tested for the effect of the manipulation following the CHANGE approach. The results were consistent with the ANCOVA approach. Hence, only the ANCOVA is reported. To avoid alpha-error inflation, Bonferroni correction for multiple testing ( Wright, 1992 ) was applied for the central statistical tests (i.e., the statistical testing of the regression coefficient for condition in each model). Therefore, the p -values of these coefficients were adjusted ( p adjuste d ) by multiplying them by the number of tests performed ( n  =   5).

For life satisfaction, the results of the conducted analyses did not suggest a significant effect (α = 0.05) of condition (β = 0.03, t [378] = 0.63, p adjusted > .99, partial η 2 = 0.001). While participants in the EG showed a significant increase in life satisfaction from T1 to T2 ( M d ifference = 0.17, t [182] = 3.44, p < .001, d =  0.25), a similar change emerged in the CG ( M Difference = 0.13, t [197] = 2.57, p = .005, d =  0.18).

Analyzing the effect of condition on participants’ positive affect, results indicated a positive effect of condition (β = 0.23, t [378] = 2.99, p adjusted = .015, partial η 2 = 0.023). The EG noted a significant increase in positive affect from T1 to T2 ( M difference = 0.18, t [182] = 3.88, p < .001, d =  0.29), while the CG did not ( M difference = 0.00, t [197] = −0.05, p = .522, d =  0.00) (see Figure 3 ).

Change in positive affect between T1 and T2 in EG and CG.

In contrast, for negative affect, results did not hint at an effect of condition (β = 0.08, t [378] = 0.88, p adjusted > .99, partial η 2 = 0.002). While the EG noted a slight but insignificant decrease in negative affect ( M difference = −0.05, t [182] = −1.33, p = .092, d =  0.10), the CG showed a significant decrease in negative affect ( M difference = −0.09, t [197] = −2.15, p = .017, d =  0.15).

Insignificant results were found for the risk or resilience factors to well-being (see Table 1 ). The analyses indicated null findings for the effect of condition on self-esteem (β = 0.01, t [378] = 0.19, p adjusted > .99, partial η 2 < 0.001) and on loneliness (β = −0.04, t [377] = −0.76, p adjusted > .99, partial η 2 = 0.002). Both groups noted an increase in self-esteem (EG: M difference = 0.06, t [182] = 2.06, p = .021, d =  0.15; CG: M difference = 0.05, t [197] = 1.76, p = .040, d =  0.12). The EG showed a slight decrease in loneliness ( M difference = −0.04, t [182] = −1.89, p  =   .030, d =  0.14), while it did not change significantly ( M difference = −0.03, t [196] = −1.49, p  =   .069, d =  0.11) in the CG.

Means and SD s of each outcome for EG and CG on each assessment

Note. Values for social connectedness represent average levels of social connectedness over all daily assessments in-between T1 and T2.

p < .05.

Beta-coefficient of condition in the respective outcome’s linear regression model within the intention-to-treat analysis.

Next, we tested if the treatment affected outcomes assumed to mediate the relationship between active SNS use and well-being: self-affirmation and social connectedness. A one-sided Welch t test revealed that participants in the EG did not show higher levels of perceived self-affirmation at T2 compared to the CG ( M EG = 3.36; M CG = 3.46, t [371.97] = −1.15, p = .874). Also, contrary to our hypotheses, compared to the CG, participants in the EG did not exhibit higher levels of social connectedness, as averaged over the six daily assessments in-between T1 and T2 ( M EG = 3.45; M CG = 3.42, t [372.76] = 0.29, p = .385).

Finally, to ensure the robustness of the findings, we performed per-protocol ( N  =   256) analyses and recalculated the analyses with compliant participants only ( N EG = 126; N CG = 130). Therefore, in the EG, participants were excluded if they were posting an equal amount or fewer pictures than usual during the study. In the CG, participants were excluded if their change in post-frequency was ≠ 0 (i.e., posting more or less than usual during the study). However, we retained the initial randomization into EG and CG. The results resembled those gathered in the intention-to-treat analyses, yielding no effect of condition on the considered well-being outcomes. Also, the effect on participants’ positive affect was not significant at the 5%-level, potentially due to the reduced sample size (β = 0.14, t [253] = 1.92, p adjusted = .281).

The results of the RCT — in most cases — did not support the ASUH. Life satisfaction, self-esteem, loneliness, negative affect, and the assumed mediating factors of self-affirmation and social connectedness seem to remain unaffected by posting pictures on one’s Instagram account. In contrast, the only outcome for which we observed a small ( Cohen, 1988 ) significant effect was positive affect. Positive affect marks the affective component of subjective well-being ( Diener, 1984 ). At least for this outcome, our results align with the ASUH. An increase in photo posting activity on Instagram led to a significant increase in positive affect, while it did not change perceptions of negative affect. This indicates that active use can slightly sway users’ positive affect even after just one week, thereby complementing research already suggesting a similar short-term effect ( Bayer et al., 2018 ).

Interestingly, our results do not suggest a similar effect on negative affect. This could indicate that active use might specifically target positive affect while leaving its seeming opposite (i.e., negative affect) unaffected. A similar pattern has been observed in other SNS and well-being studies ( Meier et al., 2020 ) and is consistent with the call for a differentiated view of well-being — including conceptualizing it not necessarily as the reverse of ill-being ( Meier & Reinecke, 2021 ; Valkenburg et al., 2022b ).

The null findings for the other outcomes contrast with some previous studies (e.g., große Deters & Mehl, 2012 ; Matook et al., 2015 ; Wenninger et al., 2014 ). For life satisfaction, some studies reported a positive association with active SNS use (e.g., Dienlin et al., 2017 ; Lee et al., 2011 ; Wenninger et al., 2014 ; Wu et al., 2021 ). However, these were mostly correlative, while an experimental study also yielded null findings ( Verduyn et al., 2015 ). It might be that some reported effects either reflected a reversed relationship (i.e., life satisfaction positively impacts active SNS usage patterns) or were caused by differences in the applied active SNS use operationalization. Moreover, contextual factors such as the valence of the content ( Locatelli et al., 2012 ) and the platform ( Teo & Lee, 2016 ) might determine how active use affects life satisfaction.

For loneliness, our null finding contrasts with some cross-sectional research that detected a link between active SNS use and loneliness ( Aydın et al., 2013 ; Lin et al., 2020 ; Matook et al., 2015 ; Yang, 2016 ). Interestingly, the findings from a study with a similar design like ours ( große Deters & Mehl, 2012 ) showed that an increase in Facebook status updates reduced perceived loneliness after one week in US students, which was further mediated by social connectedness. Notably, a conceptual replication of the study with German participants likewise failed to detect a substantial effect ( große Deters et al., 2014 ). The disparity between the findings of our study and the studies above could indicate cultural and platform differences in the effects of active use. In contrast to Facebook, communication on Instagram is mainly picture-based and may be less effective in causing loneliness alterations. Also, our findings align with some other research failing to detect an association between active SNS use and loneliness ( Burke et al., 2010 ; Dienlin et al., 2017 ; Verduyn et al., 2015 ). Importantly, current work increasingly stresses the importance of other contextual factors determining how active use can impact loneliness. Besides users’ age ( Teo & Lee, 2016 ), the amount of content also seems to influence the active use and loneliness relationship, with findings indicating even adverse effects of rampant posting activities ( Hunt et al., 2021 ; Wang et al., 2018 ).

For self-esteem, several past studies stress the importance of reciprocation by others, for instance, through likes and comments, for active use to exert a positive effect on self-esteem (e.g., Marengo et al., 2021 ; Valkenburg, 2017 ). Likewise, a positive effect was shown in previous studies in cases where users reflected upon the legacies of active use as in the viewing or editing of their profile ( Gentile et al., 2012 ; Toma, 2013 ; Toma & Hancock, 2013 ), presumably by enabling self-esteem-benefitting self-reflective processes ( Krause et al., 2021 ). However, like other findings (e.g., Steinsbekk et al., 2021 ; Wang et al., 2017 ), our results indicate that merely posting pictures seems insufficient to sway self-esteem and perceived social connectedness — deeming this often-assumed mediating mechanism less likely. Similarly, our results do not hint at active use’s potential to stimulate self-affirmation and it might need some further processing for self-esteem effects to emerge.

In summary, our results mainly do not support the claim of the ASUH that active SNS use positively contributes to well-being. While we could detect an effect of active use on positive affect, the other considered well-being outcomes did not change significantly by increasing participants’ Instagram photo posting frequency.

Our findings support the increasingly critical view on the ASUH (e.g., Valkenburg et al., 2022b ) with much-needed causal evidence from a strong research design. Our results challenge active SNS use as the beneficial counterpart to harmful passive usage. This view shaped research for a long time and was often promoted by platform providers themselves ( Docherty, 2020 ). Instead, our study mainly indicates null findings and therefore does not support active use being a significant determinant of users’ well-being.

At this point, we would also like to draw attention to the importance of reporting null findings for research in general (e.g., Ferguson & Heene, 2012 ), but especially regarding the ongoing debate on the active/passive use dichotomy (e.g., Meier & Krause, 2022 ). Besides the already voiced points of critique concerning the ASUH ( Valkenburg, 2022 ; Valkenburg et al., 2022b ), we believe it is crucial to also note the possibility that existing meta-analyses (e.g., Liu et al., 2019 ) could have overestimated effects due to unpublished null findings ( Ferguson & Heene, 2012 ; Rosenthal, 1979 ). Therefore, we deliberately decided to report effects for all outcomes considered to encourage a critical but nuanced view of the ASUH and potentially assist future meta-analyses in clarifying the overall relationship between active SNS use and well-being.

Limitations

Our study operationalized active use as the frequency of posting pictures on one’s Instagram account. This ensured that any detected effects could be traced to this activity alone. This approach, relying on a well-defined and quantifiable public active use action ( Valkenburg et al., 2022b ), helped overcome the obstacles of other ill-defined active use operationalizations ( Trifiro & Gerson, 2019 ). Nevertheless, it limits the generalizability of our findings as they do not cover the full spectrum of actions (e.g., Burke et al., 2010 ; Verduyn et al., 2017 ). Especially, more private or interactive forms of active use (e.g., chatting; Valkenburg et al., 2022b ) seem conceptually different from public active use. These have been, in some instances, shown to be more consistently and positively linked with well-being outcomes (e.g., Yang et al., 2021 ).

Likewise, we did not consider the posts’ content or tonality. However, a recent extension of the active/passive dichotomy advises accounting for the content’s tone of communication and how targeted it is toward one’s audience. The extended active - passive model assumes that warm and targeted content contributes most to well-being ( Verduyn et al., 2022 ). The extended model further aligns with recurring claims to incorporate contextual factors into our understanding of active use’s potential, including focusing on the depth and authenticity of the shared content ( Yang et al., 2021 ). Ultimately, this could explain why our study, without focusing on a specific type of communication, mostly failed to reveal effects.

We further opted for a relatively short time frame of one week. This could explain why we found an effect on a more fluctuating construct, such as positive affect. In contrast, more stable aspects of well-being, such as self-esteem, life satisfaction, or perceptions of loneliness ( Eid & Diener, 2004 ; Mund et al., 2020 ) might have been too inert to be impacted by the applied manipulation in this short period. However, we note that previous experimental studies manipulating SNS use have been able to detect effects on these constructs for a similar period (e.g., große Deters & Mehl, 2012 ) or even shorter time frames (e.g., Gonzales & Hancock, 2011 ; Vogel et al., 2014 ). Notably, most participants reported posting pictures on Instagram only rarely. Thus, they could have found a sudden increase in such unusual activity off-putting or would have needed longer acclimatization before effects could have unfolded. Nevertheless, the results of our study hint at an increase in positive affective states and thus provide evidence for a mood-enhancing impact of active Instagram use. Given the centrality of positive affect in subjective well-being ( Diener, 1984 ), it could well be that, in the long run, the positive effect of active SNS use also spills over to other dimensions of well-being. Therefore, we call for future research to examine varying time frames to further distinguish immediate, medium, and long-term effects of active SNS use.

An additional limitation concerns the increment in posting frequency in the EG. While the change in posting frequency in the EG during the study was large (i.e., 2.2 pictures more) — especially considering the group’s relatively low usual frequency of posting Instagram pictures (i.e., 0.4 pictures) — it might have still been too marginal to cause well-being changes. We explicitly opted for individualized instructions based on each participant’s usual posting frequency instead of instructing them to post a fixed number of pictures during the study. This brought several benefits: First, it ensured that all participants in the EG, regardless of how many pictures they usually post, would increase their posting frequency. Second, this approach created similar barriers for all participants to increase their posting frequency. For instance, instructing participants to post five more pictures than usual could have been a drastic change for a person that usually posts nothing compared to someone that posts several pictures daily. Finally, by letting participants freely decide how many pictures they feel comfortable posting more, we captured behavior in participants’ natural range and hence, increased the study’s external validity. Moreover, not forcing participants into an unnatural and potentially burdensome SNS activity (i.e., posting too many pictures) reduced the dropout risk. Nonetheless, this individualized instruction might have caused a change in posting frequency during the study, not powerful enough to cause sufficiently big effects on well-being. Future studies could investigate the optimal dose for active use to promote well-being.

Lastly, we would like to highlight some unique characteristics of our recruited sample. The sample included a relatively high share of females and had a fairly high mean age ( M age = 32.5). Although the ASUH does not suggest any gender- or age-specific effects, future studies must show how the findings generalize to more gender-balanced populations, other cultures, and younger users.

The study contributes to the ongoing debate on the ASUH. The results yielded a positive effect of active SNS use on positive affect but no significant effects on any other well-being measure. This underlines the need to move beyond the long-lasting dichotomizing view on SNS activities and calls for further refinements of the active/passive dichotomy to fully understand the effects of SNSs on users’ well-being. The extended active-passive model ( Verduyn et al., 2022 ) could constitute a viable starting point. At the very least, encouraging users to increase the number of pictures posted on their Instagram account — as considered in our study — does not seem to be the promising solution that researchers and platform providers have long hoped for to unlock Instagram’s potential to enhance well-being.

The data underlying this article will be shared on reasonable request to the corresponding author.

This work has been funded by the Federal Ministry of Education and Research of Germany (BMBF) under grant no. 16DII127 (“Deutsches Internet-Institut”) and grant no. 16DII131 (“Weizenbaum-Insitut e.V.”).

Conflicts of interest : None declared.

A first run of the study was conducted in August 2020. However, in that first run, in which participants were not monetarily incentivized for their participation, we encountered a systematic dropout from T1 to T2 in the EG. The analyses showed that dropped-out participants in the EG significantly differed in their level of negative affect at T1, indicating that participants with higher levels of negative affect were more likely to drop out in this group. Due to this selective attrition bias, the study’s results did not allow for valid conclusions. Therefore, we re-ran the study and took measures in the current study to prevent the reoccurrence of systematic dropout (e.g., Zhou & Fishbach, 2016 ). First, we used a monetary incentive. Secondly, we applied a stratified randomization procedure based on participants’ baseline negative affect. Participants below or above a cut-off score of 1.5 were separately randomized into EG or CG. In case of encountering a systematic dropout once again, we then could still test our hypotheses with the data of participants in the low-negative affect stratum in which — based on the results of the initial study — the risk of systematic dropout seemed less likely.

In addition, the daily questionnaires included short measures for self-esteem, mood, and loneliness for exploratory multilevel analyses that turned out to be highly underpowered to be used for further analyses.

Therefore, data of participants in the low and high affect stratum were combined for the analyses (see footnote 1).

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A systematic review of social media as a teaching and learning tool in higher education: A theoretical grounding perspective

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• Volume 28 , pages 11921–11950, ( 2023 )

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• Eva Perez   ORCID: orcid.org/0000-0002-4476-899X 1 ,
• Stefania Manca 2 ,
• Rosaura Fernández-Pascual 3 &
• Conor Mc Guckin 1  

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The use of social media in higher education has been demonstrated in a number of studies to be an attractive and contemporary method of teaching and learning. However, further research and investigation are required in order to align social media's pedagogical benefits with the theoretical perspectives that inform educational practices. It is the objective of this study to provide a systematic literature review using bibliometric analysis techniques and content analysis to provide a map of research produced between 2009 and 2021. This study aims to identify theoretical frameworks, current research trends, and patterns in this field. A total of 772 publications were analysed using bibliometric methodology, while a subset of 55 publications were analysed using content analysis. As indicated by the results, there is still a growing interest in this area of research, with recent studies still focusing on attitudes towards the use of social media in teaching and learning. According to the content analysis, technology acceptance theories and learning theories are the most commonly used reference theories. This field has yet to elaborate on pedagogical theory, and there is a tendency to rely primarily on technology acceptance models rather than pedagogical models. A discussion of future practice and research implications is also provided.

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1 Introduction

The popularity of social media, among students, has increased dramatically in recent years because of technological advances in Web 2.0 tools (Eid & Al-Jabri, 2016 ; Tess, 2013 ). Indeed, social media has attracted over three billion active users across the globe (Statista, 2022 ). Such technologies have demonstrated their potential for learning and teaching due to its functions for document exchange, virtual communication and knowledge information (Hosen et al., 2021 ; Manca & Ranieri, 2017 ). Social networking sites (e.g., Facebook, Twitter, Instagram), and online games have been widely used for information gathering and dissemination, collaborative learning, and online social and professional connections (Cao et al., 2013 ). Most recently, Manca’s ( 2020 ) review of Instagram, Pinterest, Snapchat and WhatsApp revealed that the two most common activities used for learning by students were content development and discussion for peer learning/assessment. The potential use of social media for teaching and learning activities has received an increased amount of interest and attention from the scholarly community (Barrot, 2021a ). A number of studies have presented evidence regarding the use of social media by academics for personal, professional, and teaching purposes (Johnson & Veletsianos, 2021 ; Manca & Ranieri, 2016a , 2016b ). In terms of specific social media platforms, some researchers have found that Facebook groups are an effective tool to support learning, affording benefits not offered by traditional online Learning Management Systems (LMS) (Barrot, 2018 ; Chugh & Ruhi, 2018 ; Hew, 2011 ; Niu, 2019 ). Similarly, Tang and Hew ( 2017 ) noted the potential of promoting positive learning using Twitter to access and create digital content and collaboration between learners. Recently, studies have extended towards the utility of social media platforms such as Pinterest, Instagram, and Snapchat. Manca ( 2020 ) notes that whilst these platforms have been gaining considerable attention among young people, they have been largely overlooked in the scholarly literature.

Social media, however, has also been shown to challenge traditional beliefs about education and pedagogy in schools and universities. According to some scholars (Manca & Ranieri, 2017 ), educators should pay particular attention to the following themes, primarily communication between students and teachers and professional conduct, as well as the integration of social networking practices into academic and teaching practices from a technological and educational perspective. Besides, other challenges included cultural and social factors that resulted in the erosion of teachers' traditional roles, the management of relationships with students, and privacy threats. Other factors included psychological resistance, traditional visions of instruction, a lack of technical support, perceived risks, institutional issues, pedagogical views, pragmatic reasons, and values.

Despite the increasing level of interest and the growing body of empirical research on specific uses of social media (Alshalawi, 2022 ; Manca & Ranieri, 2016c ; Sobaih et al., 2016 ), very few studies have been conducted to systematically examine how academics are utilizing social media within their teaching engagements and have mapped the use of social media in education across the various disciplinary fields (Barrot, 2021a ; Rehm et al., 2019 ).

Although social media use in higher education has become relatively common (Barrot, 2021a ), there is still much to be researched in order to develop a better understanding of its use as a teaching and learning tool (Sutherland et al., 2020 ). In fact, research has demonstrated that evidence-based pedagogical approaches informed by relevant empirical research are weak (Chugh et al., 2021 ). Thus, there is a necessity for further empirical work, grounded in teaching, learning, and educational technology theories, that can advance this growing field of education (Valtonen et al., 2022 ). The challenge for the development of a pedagogy for social media integration is to encourage robust and theoretically driven research that can explore the application of established learning theories and the facilitation of social media in teaching and learning (Churcher et al., 2014 ). Our belief is that focusing on the need for theoretical integration can help mitigate some of the shortcomings associated with the challenges described above.

The purpose of this study was to conduct a systematic review of the use of social media for teaching and learning purposes in higher education (2009–2021) utilizing bibliometric methods and content analysis. A primary objective of the study is to assess the degree of theoretical soundness of the studies published to date and to map the current state of the art in regard to the use of social media in teaching and learning.

This study focuses on two aspects of value: on the one hand, it examines the theoretical robustness of studies regarding teaching and learning processes based on the use of social media in higher education that have been published to date; on the other hand, it employs a mixed-method approach combining bibliometric analysis with qualitative analysis to examine the teaching and learning processes. It is our understanding that this is the first study that attempts to accomplish these objectives.

2 Theoretical background

2.1 learning benefits of social media in higher education.

Various studies have demonstrated the use of social media as a supportive and interactive tool for learning in higher education (Everson et al., 2013 ; Greenhow & Galvin, 2020 ; Manca, 2020 ; Manca & Ranieri, 2013 ). Some studies have focused on social media platforms such as Facebook, Twitter, and YouTube (Everson et al., 2013 ) or Instagram, Pinterest, Snapchat, and WhatsApp (Manca, 2020 ). The benefits of using social media in higher education has been shown to promote student-centred pedagogies (Camas Garrido et al, 2021 ). For example, the most commonly reported positive effect of Facebook is its capacity as a learning tool for enhanced communication, collaboration, and sharing of information (Niu, 2019 ). Indeed, Facebook groups are the most reliable feature to conduct learning activities (Manca & Ranieri, 2016c ), whereas Twitter has most commonly been used for communication and assessment purposes (Tang & Hew, 2017 ). In general, the use of social media has a positive impact on student learning. However, this is not necessarily attributed to the technologies per se, but to how the technologies are used, and how certain pedagogy and/or instructional strategy is developed (Hew & Cheung, 2013 ). As argued by Greenhow et al. ( 2019 ), educators should show clarity in studying evidence-based pedagogical approaches to teaching.

Some researchers (e.g., Churcher et al., 2014 ) have reported upon how the application of learning theories can facilitate social media integration in order to create virtual communities of practice and generate positive learning outcomes. The main focus of social constructivist learning theories is on learning as a process of active discovery and the construction of knowledge in a social and cultural context (Aubrey & Riley, 2016 ). In this line, social media support social constructivism theory (Dron & Anderson, 2014 ) as it is perceived by educators to provide direction for social constructivist teaching styles (Rambe & Nel, 2015 ). In addition, the connectivist approach views learning as a network phenomenon influenced by technology and socialization (Siemens, 2006 ), as learners are encouraged to engage in peer-to-peer dialogue, sharing resources and promote communication skills (Siemens & Weller, 2011 ). From this perspective, social media can provide a platform for mixing learning and social activities (Manca, 2020 ).

In general, while students at all levels seem to harbour positive views on academic uses and applications of social media, educators appear to be somewhat more cautious than students (Piotrowski, 2015 ). Academics are most likely to use social media for research and career development than to support learning and teaching activities (Chugh et al., 2021 ; Manca & Ranieri, 2016b ). This is likely due to the fact that it can be difficult for educators to maintain best practice of pedagogy while continuously learning how to incorporate emerging technologies (Churcher et al., 2014 ). Existing research on the use of social media in higher education has been mostly about the effectiveness of social media as a teaching and learning tool (Manca & Ranieri, 2013 , 2016b ; Tess, 2013 ), but there has been a lack of empirical data (Mnkandla & Minnaar, 2017 ) and support from theory (Al-Qaysi et al., 2020 ).

Ngai et al. ( 2015 ) argue that the development of a theoretical framework for work in this area can be supported by a combination of both technology and educational theories. Al-Qaysi et al. ( 2020 ) found that whereas the Uses and Gratification Theory (UGT: Katz, 1959 ) and the social constructivism theory (Wertsch, 1985 ) are the most widely used educational theories in social media, the Technology Acceptance Model (TAM: Davis, 1989 ) and the Unified Theory of Acceptance and Use of Technology (UTAUT: Venkatesh & Davis, 2000 ) are the most extensively used technology theories in studying social media adoption in education.

Indeed, there is a lack of theoretically based research that could lead to a coherent set of practices regarding the use of social media use in higher education. This shortcoming of theoretical development in pedagogical approaches to the use of social media in higher education has important implications also for social media literacies. Manca et al. ( 2021 ) remind us that educators who do not integrate learning theory into their teaching practices run the risk of having a superficial understanding of the construction and development of meaning in favour of centring technology.

This review of the literature purposely focuses upon research that is theoretically grounded and examines the most recurrent models and theories adopted to support pedagogical use of social media in higher education.

2.2 Systematic reviews on social media in education

The increasing number of systematic reviews related to the use of social media in education highlights the importance of these reviews in shaping educational research, identifying future research directions, and bridging the research-practice divide (Chong et al., 2022 ). Scholars have adopted several approaches to systematic reviews of scientific literature: (1) qualitative synthesis (e.g., Manca, 2020 ; Niu, 2019 ); (ii) meta-analysis (also known as quantitative synthesis) (e.g., Al-Qaysi et al., 2020 ; Mnkandla & Minnaar, 2017 ); (iii) qualitative and quantitative synthesis (e.g., Greenhow & Askari, 2017 ; Manca & Ranieri, 2013 , 2016b ; Manca et al., 2021 ; Tang & Hew, 2017 ); (iv) bibliometric analysis (e.g., Barrot, 2021a ; Lopes et al., 2017 ; Rehm et al., 2019 ); and most recently (v) mixed methods approach using bibliometric analysis and qualitative analysis (e.g., Barrot, 2021b ).

Most recent systematic reviews have utilised bibliometrics—a quantitative analysis of the bibliographic characteristics of a growing body of literature (Lopes et al., 2017 ). Although there has been an increase in the use of this approach across various academic fields, the method is relatively new to educational research (Arici et al, 2019 ; Chen, Zhou & Xie, 2020 ; Gumus et al., 2018 ; Song et al, 2019 ). In the area of our interest, there has been a paucity of research that has used the bibliographic method, even in conjunction with more traditional approaches, such as qualitative ones.

In their bibliometric analyses, Lopes et al. ( 2017 ) explored the use of Facebook in educational research, used Web of Science as the database to generate 260 articles from multiples levels of screening. The study found that most articles focused on social media, student’s learning, and case study research designs. It validated the versatility of Facebook as a platform for teaching and learning across different countries and disciples, however it did not study theories or models that can best examine Facebook acceptance.

In their bibliometric analysis, Rehm and colleagues ( 2019 ) focused on multiple social media platforms. Their findings showed that five out of the top 20 cited papers across all journals on instructional design and technology scholarship between 2007 and 2017 were on social media, indicating the growing interest in this topic within educational research.

Barrot ( 2021a ) examined the scientific literature related to the use of social media for education. They found that, out of the 15 examined social media platforms, Facebook, Twitter, and YouTube attracted the greatest attention. The data also revealed that studies on Facebook (9 out of 10) stand out in terms of citation. These findings suggest a growing interest in the use of Facebook for educational purposes. The authors suggested two possible reasons for this. Firstly, as the number of social media platforms and active users increases, so too does the number of research projects that explore their pedagogical use. Secondly, the more sophisticated the platform, the more likely it is to be used for teaching and learning.

From this review, it can be seen that only a few studies so far have mapped the scientific literature of social media in higher education using a mixed method approach – more precisely, content and bibliometric analyses. To complement and extend these earlier reviews, the current systematic review mapped the scientific literature of social media as a teaching and learning tool, giving a wider coverage to determine which theoretical frameworks can best examine the acceptance and pedagogical use of social media in higher education. Thus, the current study was undertaken to understand the landscape of scholarly work in social media as a teaching and learning tool in higher education, particularly its growth, geographical and publication distribution, speech patterns, referring to most commonly used terms or dominant terms, regarding the evolution of the term “social media”, and the analysis of theories / models that are used to examine social media acceptance and adoption in higher education.

3 Rationale and research question

In this study, social media is examined from a theoretical perspective, with a focus on studies which have used theory to help explain social media integration as a teaching and learning tool in higher education. A body of literature has developed recently that links theory with the use of social media in terms of pedagogical best practice. For example, the TAM model (Davis, 1989 ) was utilised to examine the educational outcomes of social media use in teaching (Cao et al., 2013 ), whereas social constructivism theory was used to investigate the potential of Facebook and wikis as collaborative learning tools (Churcher et al., 2014 ). Advancing previous traditional and single method approaches to reviewing literatures, this study advances a mixed-methods approach to explore connections among research articles published between 2009 and 2021. Specifically, this study addresses the following research questions:

What are the main characteristics of the scientific literature in terms of (a) year of publication, (b) publication outlets, (c) leading countries, and (d) affiliations and core authors?

What are the most frequent speech patterns and research trends within the studies?

What theoretical frameworks / models were employed in the studies to guide social media integration in education? And, which study aims are most commonly aligned with such frameworks / models?

A mixed methods approach combining quantitative (bibliometric analysis) and qualitative (content analysis) methods was used to develop a complementary picture of the research area in terms of context for trends (Plano Clark, 2010 ) and to triangulate findings in order that they may be mutually corroborated (Bryman, 2006 ). Qualitative content analysis is useful for “... the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns” (Hsieh & Shannon, 2005 ; p 1278). Bibliometric analysis is a rigorous, systematic, and innovative method for analysing publication productions and research trends (de Oliveira et al., 2019 ; Erfanmanesh & Abrizah, 2018 ). It enables the identification of relationships among different aspects of the scientific literature through the analysis of publications and documents according to specific characteristics, such as authors, journals, institutions and countries (Esen et al., 2020 ).

The analysed studies were sourced from ERIC and Web of Science and those published from 2009 to June 2021 were included. 2009 was the first recorded fit for the criteria of concern to this study, which is in line with recent studies that have highlighted that social media started to gain attention in 2010 (Valtonen et al., 2022 ). The Web of Science (WoS) was used as a search database in this study since it is the most important bibliometric database (Pranckutė, 2021 ), whereas ERIC on EBSCO databases was used as a subject specific database in education research (ERIC,  https://eric.ed.gov/?faq ).

To increase the accuracy of the current analysis, books, book chapters, and book reviews were excluded, with a focus on peer-reviewed articles, proceedings papers, and literature reviews (Leong et al., 2021 ).

The two databases were searched using the following search string:

(TS = (("social media" OR "social networking site*" OR facebook OR twitter OR Instagram)) AND TS = (("higher education" OR "third level" OR universit* OR college OR academic*)) AND TS = ((teaching OR learning OR "educational tool*"))) AND ((LA == ("ENGLISH")) NOT (DT == ("BOOK" OR "BOOK REVIEW" OR "BOOK CHAPTER"))

This study methodology is based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Moher et al., 2009 ). PRISMA supports a transparent approach for systematic reviews and ensures a replicable procedure (e.g., review protocol, search strategy, article selection criteria). When considering the criteria for inclusion and exclusion of literature the emphasis was upon studies assessing the use of social media as a teaching and learning tool and not, for instance, as a marketing / communication too. In addition, studies focused on English as a second language were excluded as these are often seen as courses that provide support to leaners, rather than leading to a defined exit award per se. Table 1 presents the screening criteria.

The first screening of sourced articles ( N  = 4,277) involved analyses of titles and abstracts. This process resulted in 812 records. Some reasons for exclusion included: studies related to studying English language; use of social media for communication purposes; studies focused on cyberbullying; social media addiction; social media marketing.

The second level of screening involved checking the full paper, classifying the study in terms of sources and to identify theoretical frameworks or models—hence selecting them for the content analysis. This resulted in 772 records, which were all eligible for bibliometric analysis. The following four characteristics were most predominant: (i) studies presenting a theoretical framework / model ( n  = 55), (ii) empirical studies about teaching and learning without theory ( n  = 221), (iii) studies about perceptions and attitudes without theory ( n  = 424), and (iv) conceptual studies ( n  = 72). For the content analysis, only the 55 studies that utilised a theoretical framework / model were included (Fig.  1 ).

The PRISMA flowchart

4.1 Procedure

Analyses commenced with bibliometric analysis of the 772 articles obtained through the second screening, identifying the main characteristics of the selected publications (year of publication, publication venues, authors, institutions, countries, and most frequent used terms).

Network visualization displaying the relationships among the main words used in abstracts were created using the VOS clustering technique (Van Eck & Waltman, 2010 ). VOSViewer software provides distance-based maps and identifies the clusters of co-occurring words, enabling identification of most used terms and the relationships between them (Van Raan, 2019 ; Waltman et al., 2010 ).

To display the dominant terms, full counting method has been considered (Leydesdorff & Park, 2016 ). Thus, each publication has the overall weight equal to Ni (Ni being the total number of terms in the “i”-publication) and each term has a weight of 1. The size of the circle and the label in the map is associated with the weight of a term. In general, the stronger the relationship between two terms, the closer they are located on the map. We have considered the “total link strength attribute”, which indicates the total strength of a term’s links with other terms (Gutiérrez-Salcedo et al., 2018 ). Whilst curved lines on the maps represent the links between terms, colours are used to indicate the cluster to which each term belongs.

Finally, the evolution of “social media” and other main terms used in abstracts were analysed and presented with the overlay visualization in Vosviewer (terms are coloured based on their year of publication). We used the viridis colour scheme obtained from Matplotlib, where by default, colours range from blue-green to yellow scheme.

For the second analytic component of the study, qualitative content analysis methods were applied to the 55 studies resulting from the second screening. The objectives were to gain an in-depth understanding of the theories/models employed in the studies and to identify the main research aims linked to the employed theories/models. Content analysis was based on a number of categories which were adapted from Manca and Ranieri ( 2013 ) and derived from analysis by author 1 and author 2. This process resulted in the following categories: (i) attitudes of social media as learning tool (studies which main aim was to investigate students’ or instructors’ attitudes towards the use of social media); (ii) social media as a supportive learning tool (studies that supported active collaborative learning, student engagement, effective communication, enhancing group task performance); (iii) efficacy of social media as learning tool (studies that focused on the impact of social media on different aspects of teaching and learning, such as: community building and informal learning). For the purpose of ensuring a level of reliability, an iterative process of analysis was carried by author 1 and author 2, and the individually derived codes were double-checked by comparing results. Once the set of codes had been recognised, dataset coding reliability was calculated (Cohen’s k = 0.85). The disagreement was resolved with discussion and subsequent consensus.

5.1 Study characteristics

Figure  2 provides the time evolution of the annual scientific production for the period analysed. The number of publications shows an upward trend until 2018, with two relatively higher values in 2015 and 2018. A slight decline is observed from 2019 onwards. The sharp drop during 2021 is due to the fact that the study covered the period between January and June of that year. We have applied a segmented linear regression (Liu et al., 1997 ), with two break points, in 2015 and 2018 (Liu & Qian, 2009 ). The segmented least squares forecast for the year 2021, provides an estimated annual value of 74 publications with a high reliability (R 2  = 0.94).

Number of papers on social media as a teaching and learning tool (2009–2021). *estimated value in 2021

Table 2 shows the number of publications by journal (conferences proceedings were not included). This represents the distribution of the journals with a production of seven or more records involving 91 publications (11.7% of the corpus). It was found that Computers & Education and Education and Information Technologies have published the most articles on social media as a teaching and learning tool, with a total of 18 articles each. The Australasian Journal of Educational Technology , Computers in Human Behaviour , and Internet and Higher Education had 13, 12, and 9 related articles, respectively.

The scholars who published the most articles are presented in Table 3 . Overall, the data set containing the 772 articles comprises a total of 2,754 authors. For the purpose of this particular set of analyses, details about professional profile and number of publications are focused on journals only. The average number of co-authors was 3.56. Therefore, authors with more than four relevant published articles were considered core authors in the aforementioned field. The list is a combination of nine leading and emerging scholars from wide geographical areas. As shown, three scholars are from universities in Malaysia, three from Romania, one from Hong Kong, one from Italy, and one from South Africa. The disciplinary areas of the core authors represent a variety of disciplines, with many of these related to the education and technological fields.

5.2 Dominant terms and research trends

The final part of the bibliometric analysed the most frequently represented words in abstracts to identify most used terms and research trends (Han & Ellis, 2019 ; Leung et al., 2017 ). Firstly, the empty words (e.g., connectors, conjunctions, prepositions, articles, adjectives) were omitted. Secondly, words whose frequency was less than 20 occurrences in abstracts were considered not relevant to the research and were excluded. Synonyms and acronyms were associated. Finally, 305 terms with the largest levels of occurrence in the abstracts were included in the analysis from a total of 22,079 words. The analysis of these terms is illustrated in Fig.  3 and Fig.  4 by means of five clusters, each represented by a different colour. The distribution of the number of keywords by year of publication is presented in Fig.  4 .

Most used words found in abstracts

Evolution over time of terms in abstracts

The word student was the most commonly used word in the abstracts ( n  = 2,156), followed by social media ( n  = 1,077), use ( n  = 1,043), Facebook ( n  = 858), and learning ( n  = 667) (see Table 6 in Appendix A for terms with more than 120 occurrences). These results indicate that the articles mostly focused on Facebook use as a social media for learning. Furthermore, the platforms that attracted the greatest attention were Facebook ( n  = 858) and Twitter ( n  = 274). Figure  3 shows the most used word in abstract. As can be seen, the high impact term “student” presents strong connections with use, social media, learning, technology, tool, social network, group, Facebook, and Twitter. Five clusters of terms were discovered as part of the visualization. Each cluster was constituted from a set of terms that are clearly delimited by their location in the map. These clusters reveal the presence of five thematic strands in the literature that focus on: (i) “student-education-platform-process-communication” (colour red); (ii) “Facebook-Twitter-participation-interaction” (green); (iii) “Learning-Use-Technology” (blue); (iv) “social media-university-social media use-social media platform-educational use” (yellow); and (v) “academic attitude-performance-intention-usefulness-satisfaction” (purple).

When the distribution of these words is shown on a year-by-year basis (Fig.  4 ), it is revealed that studies focused on the study of Facebook page, Facebook use, informal learning, and peripheral terms such as blog, community, video, or web, is located in the initial years under study. High impact terms such as Facebook, student, learning, use, education, or social network are published on average in studies between 2014 and 2016. The term “social media” is introduced from 2016, in papers between 2017–2018, linking it to terms such as “data”, “educational use”, and “educational tool”. From 2018 onwards, the focus of the studies is towards “attitudes”, “influence”, “intention”, “performance”, or “satisfaction”.

Four research trends are identifiable throughout the period of study (Table 4 ). From 2010–2014, studies were mainly focused on Facebook as a community of practice, blog, and for informal learning. From 2014–2016, Facebook was still relevant, but studies had more emphasis on the educational learning process of the use of Facebook by students. During the period of 2016–2018, the term “social media” peaked and studies were focused on social media for education and as an educational tool. From 2018 onwards, the focus of the studies was towards “attitudes”, “influence”, “intention”, “performance”, or “satisfaction”.

5.3 Theoretical frameworks/models

The findings show that only 55 studies out of 772 cited a theoretical framework or model, this is only 7% of total number of studies. Content analysis was used to analyse more in-depth information about the 55 selected papers. A total of 16 frameworks/models were identified. They were grouped into six categories of similarity. These are shown in Table 5 in relation to the number of citing studies per category. The number of citing studies is higher than the sample size ( n  = 55) because there are some studies that uses more than one framework/model. The most cited theoretical framework/model was technology acceptance models which were cited in 41 studies. This is followed by learning theories cited in 11 studies. Social capital theory/innovation diffusion theory is cited in 5 studies; uses and gratification theory/social gratification theory cited in 3 studies; lastly, Information systems success model/communication theory and theory of reasoned action/theory of planned behaviour are only cited in 2 studies, respectively.

Figure  5 shows the use of the main framework(s)/model(s) categories from 2013 to 2021. Figure  5 highlights that studies began citing theory in 2013, with further significant increases identifiable in 2017 and 2020. It also indicates that technology acceptance theories are predominantly the most employed theories in all years, 2020 having the highest publications.

Theoretical frameworks/models over time

The 55 studies were further analysed by study aims which were categorised using the following classification: (1) attitudes of social media as learning tool ( n  = 32); (2) social media as a supportive learning tool ( n  = 16); (3) efficacy of social media as learning tool ( n  = 7). The study aims over time are revealed in Fig.  6 . The results indicate that publications with the aim of investigating attitudes of social media as a learning tool are the most common with 2017 being the most popular year of publication.

Research aims over time

Finally, to represent the empirical relationships among the aims and the theoretical frameworks/models, a word co-occurrence analysis providing a similarity matrix was carried out (Hu et al., 2013 ). A measure of similarity is obtained by counting the co-occurrences (Yang et al., 2012 ), which makes it possible to represent the relationships (conceptual clustering) that exist among the aims and frameworks/models (Chen et al., 2019 ). Direct lines represent connections between the theoretical frameworks/models. Figure  7 indicates that the strongest relationship is presented by studies with the aim to explore attitudes of social media as learning tool by integrating a technology acceptance model. This is followed by information and communication theories being used to explain the efficacy of social media as learning tool. Learning theories are mostly related to studies that are aimed at exploring social media as a supportive learning tool.

Research aims & theoretical frameworks/models network

6 Discussion

The current study has mapped the scientific literature regarding the use of social media in higher education teaching and learning (2009 to 2021). The central aim was to document research trends, dominant terms, and the main characteristics of studies, with a focus on providing a new perspective on the theoretical groundings that may explain the pedagogical integration of social media within higher education teaching and learning.

These results extend the findings of other systematic literature reviews regarding social media use in education-conducted on single or multiple platforms (Lopes et al., 2017 ; Manca, 2020 ; Tang & Hew, 2017 )-and across various disciplinary fields (Barrot, 2021a ; Rehm et al., 2019 ). The main finding indicates a shift from studies focused on Facebook, as the most researched social media platform and its use by students for informal learning, to a more recent trend from 2018 onwards showing studies still focused on attitudes, intentions, and satisfaction of social media as a teaching and learning tool. This is aligned with results from the content analysis which showed that only a minority of studies report the use of theory, and those that do report research aims based on the investigation of attitudes towards social media as a learning tool by integrating a technology acceptance model.

The following sections discuss the three research questions of this study in relation to results concerning both the use of social media as a teaching and learning tool and its pedagogical integration.

6.1 Characteristics of the scientific literature

Overall, the data show a constant growing trend in the number of publications concerned with social media use in teaching and learning, with an increase in two different years (2015 and 2018). This trend confirms a growing interest in the research community regarding the use of social media as a teaching and learning tool (Bodily et al., 2019 ; Valtonen et al., 2022 ). One of the reasons for the rapid growth of research in this field may be related to the relevance of social media platforms in students’ daily lives. We anticipate that further studies will be conducted as new social media uses and applications increases. For example, since its launch in 2017, TikTok has become the fastest growing social media platform worldwide, reaching nearly 83 million monthly active users as of February 2021 (Statista, 2021 ). From an educational perspective, TikTok has proven to be an effective pedagogical tool in corporal expression courses (Escamilla-Fajardo et al., 2021 ) and for political participation and civic engagement (Literat & Kligler-Vilenchik, 2021 ).

In terms of publication venues, Computers & Education , which is an international peer reviewed journal and one of the most prominent journals on the use of technology in education (Arici et al., 2019 ), has published the highest number of papers. The majority of the publications are also international, implying that educational research in social media is pedagogically used in local, regional, or international learning contexts (Barrot, 2021a ).

Geographically, results showed widespread interest across different countries, with more than half of the studies conducted outside of Europe. Whilst Barrot ( 2021a ) has reported that the US was by far the leading country in this field, Manca ( 2020 ) found that most of the research was from the Middle East.

6.2 Dominant terms and research trends

Based on the clusters of terms identified from the analysis of the most used words in abstracts, the platforms that attracted the greatest attention were Facebook and Twitter. In her review, Barrot ( 2021a ) also found that these platforms were the most popular, and suggested that Facebook and Twitter are more likely to be used for teaching and learning as they offer multiple affordances when compared to other less developed/newer platforms.

While the phenomenon of social media remains relatively new to academia research, it has grown in popularity throughout the analysed period. In the initial years, the literature showed evidence of research on the use of social media for informal learning (e.g., Forkosh-Baruch & Hershkovitz, 2012 ) through Facebook (e.g., Hew, 2011 ), and blogs (e.g., Zinger & Sinclair, 2013 ).

In our corpus of literature, the term “social media” starts to flourish from 2016. Many studies with a focus on the use of social media as an educational tool started to be published in that timeframe (e.g., Balakrishnan, 2017 ; Manca & Ranieri, 2016a , 2016b ; Sobaih et al., 2016 ). From 2018 to 2021, research trends were more focused on studies about attitudes and satisfaction, confirming trends from earlier studies on attitudes regarding Facebook (e.g., Manca & Ranieri, 2013 , 2016a , 2016b ). Manca and Ranieri ( 2016c ) argued that whilst there was a favourable attitude towards social media use for education, many academics would express a preference for using social media for personal and professional use, rather than for teaching and learning purposes.

6.3 Theoretical frameworks/models and study aims

The third research question examined the studies which had included a theoretical framework/model to explain the integration of social media in learning and teaching. The findings show that only 55 studies out of 772 cited a theoretical framework or model. This result demonstrates a general lack of theoretically based research. This concurs with the findings of Manca et al. ( 2021 ) who concluded that studies that do not integrate learning theory run the risk of superficial understanding of the pedagogical advantages of social media for learning and teaching.

Our findings show that 16 theoretical frameworks/models guided the 55 studies, with the technology acceptance models being the most frequently used. These theoretical frameworks/models were present in 41 studies. Thus, with the overwhelming presence of technology acceptance models, future research should endeavour to adopt other theoretical frameworks/models to verify the results obtained from TAM and its variants. For example, Al-Qaysi et al. ( 2020 ) argued that the development of a theoretical framework that can best examine the integration of social media for learning and teaching can be justified by the use of the uses and gratification theory (Katz, 1959 ) and the social constructivism theory (Wertsch, 1985 ). Furthermore, the use of social media for teaching and learning should be a pedagogical decision and not a technology one (Everson et al., 2013 ). Considering that educational technology research to date has aimed to understand the integration of, and factors affecting, technology use, mainly by employing theories from psychology and information systems, it was found in a recent study by Valtonen et al. ( 2022 ) that the largest amount of educational research targeted how technology can support learning processes based on different learning theories. This is in contrast with our findings which have shown that technology acceptance theories are the most studied frameworks/models in social media for teaching and learning. The reason for this contradiction is that Valtonen et al.’s ( 2022 ) review identified studies with an educational technology focus and not on social media specifically. Indeed, technology research’s history is long, rich and broad (Weller, 2020 ). However, this indicates that the use of socially oriented theories of learning and constructionist tradition within various technology-enhanced contexts and environments is the most common fit to understand technology integration.

Aligned with our findings is the work of Ngai et al. ( 2015 ) and of Chintalapati and Daruri ( 2017 ) who declared that the Technology Acceptance Model (TAM) is widely used in social media research to explain the acceptance of social media and to measure the factors that influence its adoption.

Our findings also show that the second most employed theoretical framework/models were those related to learning theories. In particular, social constructivism theory was the second most cited approach. These publications peaked from 2017, indicating that the use of learning theories is still in its infancy. Greenhow and Askari ( 2017 ), who assessed the state of social media research in education, found that the major gap in studies was concerned with the link to concrete measures of learning. This finding aligns with an earlier review study that noted increasing interest for social media use, but insufficient empirical support for claims that such technology can be an effective learning tool (Tess, 2013 ). Reflecting on these findings, Greenhow et al. ( 2019 ) suggested that research should focus on practices, outcomes, and learning across different contexts.

As social media is an emerging technology, it is important to continually understand attitudes towards it. Hence, it is not surprising that most of the studies in our analysis were designed to investigate the perceptions and attitudes of students and academics towards the use of social media as a learning and teaching tool. In theory, this is best explained by using an information systems theory such as the TAM (Ngai et al., 2015 ). However, this does not explain best practice when introducing social media as a learning and teaching tool. Many studies in the analysis which cited learning theories used TAM with social constructivism theory to examine collaborative learning and engagement through social media use (Alalwan et al., 2019 ; Alamri et al., 2020b ; Al-Rahmi, et al., 2018 ).

Since Technology acceptance theories are designed to examine teachers’ and students’ readiness to incorporate social media into teaching and learning practices, it is not surprising that they are aligned with attitudes towards social media as a teaching and learning tool. However, it appears that academic research has not much progressed in terms of providing better theoretical strength to pedagogical models and teaching practices.

The second most commonly found research aim in the studies was related to active collaborative learning, student engagement, effective communication and enhancing group performance. This research aim was supported by learning theories. For example, Yu et al. ( 2010 ) investigated student engagement on Facebook from a pedagogical standpoint based on social learning theory. Al-Rahmi et al. ( 2015 ) explored the factors that contribute to the enhancement of collaborative learning and engagement through social media based on the theory of social constructivist learning. This is in line with Churcher et al. ( 2014 ) study who argued that using social constructivist theory has the ability to develop a community of practice, and maximize learning potential.

Lastly, only 7 studies focused on the efficacy of social media as a learning tool which are supported by information and communication theories. For example, Chaka and Govender ( 2020 ) tested the implementation of mobile learning using Facebook as a medium of communication using a combination of the unified theory of acceptance and use of technology (UTAUT) model, Information Systems (IS) success model and the educational use of Facebook theory. Al-Rahmi et al. ( 2018 ) investigated the use of social media to encourage sharing knowledge, information, and discussion based on constructivism theory, technology acceptance model, and communication theory.

7 Conclusion and implications

The purpose of this study was twofold. First, we aimed to reveal research trends and most commonly used terms of social media for teaching and learning in higher education. The journals that published the most related papers, core scholars working on this field, and the countries in which the related research was based by employing a bibliometric analysis of the research. This analysis suggested that this research field is growing rapidly and evolving. This may be explained by the fact that social media have revolutionized the life of many people and thus attracting much attention.

Second, we employed content analysis to provide a new perspective on the theoretical groundings of the articles in the field. The results showed a lack of theoretical based research in this field, with some evidence of technology acceptance models and learning models as key theories that best explains the integration of social media as a teaching and learning tool.

Although the current study has provided useful insights regarding social media use in teaching and learning, some limitations need to be acknowledged. First, this study was not intended to report, discuss and analyse the findings of each study included in this review. Instead, it aimed to provide some numerical evidence that show the evolving research trends of social media for teaching and learning, as well as the frameworks/models studied and purpose of those focal studies. Second, this study analyses only the articles indexed in the WoS and ERIC database. Therefore, future studies could include articles from Scopus database, book chapters, book reviews, or other publications outside the chosen database. Thirdly, social media research is in its early stages, therefore new studies will continue to surface and continued proliferation of new social media technologies (Ngai et al., 2015 ). More recent social media in education research should be considered in future studies. Finally, future research could explore other research perspectives like research methods and contexts/disciplines.

This paper provides a new perspective on the theoretical groundings in the field of social media as a teaching and learning tool. Several implications can be drawn from this. Firstly, most studies are focused on investigating students and/or instructors’ attitudes towards the use of social media by integrating technology acceptance models. Future studies should focus on “best practice” for integrating social media into pedagogy, tied to student learning outcomes by integrating learning theories. Such studies may also help shape future research on social media integration in formal education, resulting potentially in solutions to educational problems rather than technological ones. Secondly, it was noted that studies employing technology acceptance models may be overwhelming the greater body of literate at present, and therefore any future research should look at post-acceptance studies, such as the impact of usage on learning and/or issues relating to it (such as privacy, security, and trust) (Manca & Ranieri, 2016b ). Finally, this study provided a review of the research landscape on the use of social media as a teaching and learning tool which can be used as a baseline in further advancing the field towards its full maturity.

As interest among scholars increases in using social media for teaching and learning, questions to consider for further research include the following: Can social media that are designed commercial purposes support learners in an educational environment? What does the adoption of social media mean from a theoretical perspective? In this regard, future work should address the pedagogical practices which are suitable for use with social media based on sound theoretical groundings.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Perez, E., Manca, S., Fernández-Pascual, R. et al. A systematic review of social media as a teaching and learning tool in higher education: A theoretical grounding perspective. Educ Inf Technol 28 , 11921–11950 (2023). https://doi.org/10.1007/s10639-023-11647-2

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• Holly B. Shakya
• Nicholas A. Christakis

Online social interactions are no substitute for the real thing.

Research has long suggested that social media can be harmful to users’ wellbeing. But past studies have often failed to acknowledge people’s baseline sociability or social media usage levels. In a comprehensive new study, the authors examined the impact of Facebook usage on wellbeing over time, and found that using Facebook was consistently detrimental to mental health. Specifically, constant exposure to people’s carefully curated posts led people to make negative comparisons to their own lives, and the sheer quantity of social media interaction often detracted from their ability to enjoy real-life experiences. Although social media can often feel like meaningful social interaction, this research demonstrates that it’s no substitute for the real thing.

The average Facebook user spends almost an hour on the site every day, according to data provided by the company last year. A Deloitte survey found that for many smartphone users, checking social media apps are the first thing they do in the morning – often before even getting out of bed. Of course, social interaction is a healthy and necessary part of human existence. Thousands of studies have concluded that most human beings thrive when they have strong, positive relationships with other human beings.

• HS Holly B. Shakya is an Assistant Professor of Global Public Health at UC San Diego. She specializes in social network analysis and social norms theory, and is currently on an NIH funded project to understand the social network and social normative determinants of adolescent fertility in the developing world.
• NC Nicholas A. Christakis  directs the Human Nature Lab at Yale University and is the Co-Director of the Yale Institute for Network Science. He is the Sol Goldman Family Professor of Social and Natural Science, appointed in the Departments of Sociology, Medicine, Ecology and Evolutionary Biology, and Bioengineering at Yale University.

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• Key Takeaways

High-quality images, videos, and infographics are vital for capturing attention and conveying messages effectively on social media.

Deep understanding and segmentation of your target audience allow for personalized and impactful messaging.

Utilize analytics to inform your social media strategies, ensuring content and ads are optimized for maximum engagement and ROI .

Effective social media campaigns hinge on visual content, compelling narratives, and audience targeting.

Influencer marketing and data-driven decisions can significantly impact campaign success.

The rise of social media has transformed digital marketing. Today, social media campaigns are essential for brand success. They can reach a global audience, boost engagement, and deliver results. The key is to run effective campaigns. This article acts as a guide, using real case studies to explain how.

We will now explore the strategies behind successful campaigns. The topics include visuals and storytelling. They also cover targeting, ads, influencers, and data use. They cover cross-platform planning and crisis management. We’ll also cover how to measure success with KPIs. Each section includes case studies to provide practical insights for marketers and businesses.

Introduction to Social Media Campaigns

Social media is crucial in digital marketing. It helps businesses reach their audiences. A “social media campaign” is a structured marketing effort across various platforms. This article will explain the importance and relevance of these campaigns.

• The Role of Social Media in Modern Marketing

Social media platforms like Facebook, Instagram, Twitter, and LinkedIn are now key to business marketing. They help engage a worldwide audience, boost brand visibility, and enhance loyalty. Being on these platforms unlocks social media marketing’s vast potential.

• Importance of Effective Campaigns for Brand Success

The success of a brand often hinges on its ability to effectively utilize social media. The digital era brought a big change in how consumers act. They now turn more to social platforms for product info, reviews, and recommendations. Therefore, brands that can create and run great social media campaigns have a competitive edge. In this article, we delve into case studies. They highlight the real impact of well-run campaigns on brand success.

• Overview of the Article’s Focus on Case Studies

This article shows how to run successful social media campaigns. It uses real cases as examples. These examples reveal how companies meet marketing goals using social media. By studying successful campaigns, readers learn about key strategies and results.

• The Value of Real-World Examples in Learning

Case studies uniquely blend theory with practice, making them a great learning tool. They show how to use marketing concepts in real situations. They offer lessons for personal marketing efforts. Studying these cases teaches readers patterns. They learn best practices and pitfalls to avoid in social media campaigns.

The Power of Visual Content

Visual content is vital in today’s digital world, especially for social media campaigns. It grabs attention, quickly conveys messages, and leaves a lasting impact. In this section, we’ll look into visual content’s many facets. We’ll also see how it enhances social media campaigns.

• Utilizing Eye-Catching Images and Graphics

Visual content begins with captivating images and graphics. These include striking photos, intriguing illustrations, or attention-grabbing infographics. They quickly engage the audience. Marketers must pick visuals that match their brand and goals. We will also highlight how the right visuals can shape a campaign’s tone and leave a lasting impression.

• The Impact of Video Content

Videos now rule social media. They come in various lengths, from short clips to longer videos. Notably, they engage audiences well. This article explores their use in social media campaigns. It will discuss the benefits of video marketing. For example, it can share complex ideas, evoke emotions, and boost audience interaction. Additionally, it will showcase successful campaigns through case studies.

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Explore 'State of Technology 2024' for strategic insights into 7 emerging technologies reshaping 10 critical industries. Dive into sector-wide transformations and global tech dynamics, offering critical analysis for tech leaders and enthusiasts alike, on how to navigate the future's technology landscape.

• Infographics as a Storytelling Tool

Infographics are great for social media stories. They quickly present information in an attractive way. Here, we will see how they can effectively share data, statistics, and stories. Also, we’ll look at the key design principles. We’ll then share examples of successful campaigns. These campaigns used infographics to educate and engage their audiences.

• User-Generated Content and Its Authenticity

User-generated content (UGC) has become popular for building trust and authenticity. It’s content by customers or brand fans, sharing real experiences and testimonials . We’ll explore its impact on social media campaigns. It boosts authenticity and credibility. Case studies will show how brands have used UGC to strengthen relationships with their audiences.

• Case Studies Showcasing Successful Visual Campaigns

We will show successful social media campaigns using visuals. These include images, videos, infographics, and user content. They’re compelling and effective. We’ll explain how businesses and organizations used these elements. By looking at these stories, readers will learn the strategies and tactics for engaging audiences.

Crafting Engaging Content

In the realm of social media campaigns, crafting engaging content is undeniably a cornerstone of success. Your content is the bridge that links your brand with your audience. It’s crucial to ensure that this link is not just made but also made strong. Here, we delve into the intricacies of creating content that captivates and resonates with your target audience.

• Strategies for Creating Compelling Content
• Know your audience. Engaging content starts with understanding them well. Take the time to research their preferences, interests, and pain points. Tailoring your content to address their specific needs is key to engagement.
• Storytelling Techniques: Storytelling is a powerful tool in the world of content creation. Learn how to weave narratives that not only convey your brand’s message but also evoke emotions and create a memorable impact.
• Building Brand Personality Through Content
• Consistency in Branding: Effective content aligns with your brand’s identity. Explore how to keep messages, tone, and visuals consistent. Do this across all your social media platforms. This will strengthen your brand’s personality.
• Showcasing Brand Values: Engaging content often reflects your brand’s values and mission. Discover ways to infuse your content with your brand’s ethos. This will create a stronger bond with your audience.
•  Interactive Content and Engagement Tactics
• Interactive content , like polls and live sessions, boosts engagement. We’ll show you how to use these in social media to increase participation. Also, response speed, contests, and user-generated content are key tactics for active engagement.
• Case Studies Exemplifying Effective Content Strategies

We’ll use successful social media campaigns to show how to create engaging content. These case studies will highlight strategies that boost engagement and results.

Creating engaging content is a continuous effort. It needs creativity, audience understanding, and adaptability. By applying the strategies in this section, you can make content that grabs and retains your audience’s attention. This approach boosts your campaigns’ success.

Targeting the Right Audience

In social media campaigns, success depends on knowing and targeting the right audience. Identifying your audience is like building a foundation. It sets the stage for your efforts. Here, we explore key aspects of audience targeting. Our goal is to help your social media campaigns succeed.

• Importance of Understanding Your Audience

Before starting a social media campaign, it’s crucial to know your audience well. This step involves deep market research. It is to learn about their age, gender, location, and interests. Understanding your audience helps you create tailored content and messages. The case studies show how brands researched their audience well. This led to successful campaigns.

• Segmenting Your Audience for Personalized Messaging

Once you’ve identified your broader audience, the next step is segmenting it into smaller, more specific groups. This segmentation allows you to craft personalized messages that cater to the unique needs and preferences of each group. We’ll explore various segmentation strategies, including psychographic, behavioral, and geographic segmentation. Real-world examples will show how brands divided their audience. They then delivered tailored content for maximum impact.

• Data-Driven Targeting Strategies

In today’s data-driven landscape, harnessing the power of analytics is paramount. We’ll discuss how data can inform your targeting strategies, enabling you to make informed decisions. By studying user behavior, engagement, and conversion, you can refine your targeting. This will help you reach the right people at the right time. Case studies will highlight instances where data-driven targeting resulted in remarkable campaign success.

• Case Studies Demonstrating Successful Audience Targeting

This section will showcase case studies on audience targeting. It will feature real examples from top brands. You’ll see how they found, divided, and used audience data for successful social media campaigns. These stories will offer tips for your campaigns. You’ll learn to attract and engage potential customers effectively.

Leveraging Social Media Advertising

Social media advertising has become an integral component of modern digital marketing strategies . Businesses need to understand paid promotion, ad formats, budgeting, and measuring ROI. This is key to using social media ads well. In this section, we will delve into key aspects of leveraging social media advertising for successful campaigns.

• Paid vs Organic Reach on Social Media

One of the fundamental decisions in social media advertising is whether to rely on organic reach or invest in paid promotion. Organic reach is the audience your content naturally reaches without paid promotion. Paid reach involves allocating a budget to boost content visibility. It’s crucial to strike the right balance between these two approaches. Organic reach is limited, and algorithms frequently change, making it challenging to reach a wider audience. Paid reach, on the other hand, offers more control over targeting and exposure but requires a financial investment. Businesses must assess their goals, target audience, and available resources. They do this to find the best mix of paid and organic strategies.

• Ad Formats and Placements

Social media platforms offer a diverse range of ad formats and placements to cater to different campaign objectives. Understanding these options is essential for crafting effective advertising strategies. For instance, Facebook provides options such as image ads, video ads, carousel ads , and more. Instagram offers sponsored posts and stories. LinkedIn specializes in B2B advertising, while Twitter has promoted tweets. Every platform has its own strengths and audience. Thus, it’s crucial to choose the right ad format and placement. These choices should match campaign goals. Also, creating engaging ad content for the chosen format is key. It boosts the campaign’s effectiveness.

• Budgeting and ROI Tracking

Allocating a budget for social media ads is strategic. It affects campaign reach and outcomes. It involves setting aside funds for ad spend, creative production, and campaign management. Establishing a clear budgeting strategy ensures that resources are utilized efficiently.

Additionally, tracking ROI is essential to measure the effectiveness of advertising efforts. Tools like Google Analytics and social media platform insights provide data on ad performance. They include click-through rates, conversion rates, and cost per acquisition. Businesses that monitor these metrics regularly can make data-driven adjustments. They can also optimize their advertising spend.

• Case Studies Showcasing Successful Social Media Advertising Campaigns

This section discusses some concepts. We will delve into real-world case studies to illustrate them. The studies exemplify successful social media ad campaigns . These case studies will provide real examples of businesses. They effectively used paid promotion. They chose the right ad formats and budgeted well. They achieved measurable ROI. By reading these success stories, readers can gain insights and inspiration for their own social media ads.

Harnessing the Power of Influencers

In social media campaigns, influencers play a crucial role. They boost brand engagement and reach. This section dives into influencer marketing . It provides tips on how brands can best work with influencers.

• Identifying the Right Influencers for Your Brand

To start a successful influencer marketing journey, you must find influencers who match your brand’s values. They must also match your target audience and goals. This involves meticulous research to pinpoint individuals whose content resonates with your niche. The right influencer will not only bring authenticity to your campaigns but also enhance credibility.

• Collaboration and Partnership Strategies

Once you’ve found potential influencers, the next step is to start collaborations and partnerships. Successful influencer marketing hinges on building authentic relationships with influencers. Brands must craft compelling proposals, outlining the mutual benefits of the partnership. Effective communication, transparency, and negotiation skills are essential in establishing a fruitful collaboration.

• Measuring the Impact of Influencer Campaigns

The effectiveness of influencer campaigns goes beyond mere follower counts. In this section, we delve into the metrics and tools required to measure the true impact of influencer marketing. Brands must monitor engagement, clicks, conversions, and sentiment. This helps measure campaign success. Equally important is calculating ROI. They should also align influencer activities with business goals.

• Navigating Potential Pitfalls

Influencer marketing is not without its challenges. Brands should prepare for influencer controversies, authenticity concerns, and algorithm changes. This section offers strategies to reduce risks and keep influencer partnerships strong.

• Case Studies of Effective Influencer Marketing

We will show you how influencer marketing works. First, we’ll present successful brand stories with influencers. Then, we’ll explain how these influencers boost brand messages. They increase engagement and aid in campaign success. Finally, each example offers lessons for your influencer marketing strategy.

Data-Driven Decision Making

In today’s digital age, data plays a pivotal role in the success of social media campaigns. Marketers and businesses can’t rely only on intuition and creativity. They must use data to make informed decisions that drive results. This section explores the significance of data-driven decision making in social media campaigns and its s.

• Importance of Data in Campaign Decisions

Data serves as the foundation upon which effective social media campaigns are built. It provides valuable insights into audience behavior, content performance, and campaign reach. Marketers can use data to find trends, preferences, and opportunities. This lets them tailor their strategies for maximum impact.

Analyzing data shows businesses the best social media platforms. It also shows them the best content and posting times for their target audience. This data helps in making marketing decisions. It ensures resources go where they are most effective.

• Key Metrics to Monitor for Success

To make data-driven decisions, it’s crucial to identify and monitor key performance metrics. Metrics vary by campaign goals. Common ones include engagement rate, click-through rate, and conversion rate. Also, return on investment (ROI). Each metric provides unique insights into campaign performance.

A high engagement rate shows your content is popular. However, a low conversion rate means you need to optimize. By tracking these, marketers can quickly judge campaign success. They can then tweak efforts to boost results.

• Tools for Social Media Analytics

The availability of advanced analytics tools has made data analysis more accessible to businesses of all sizes. The platforms offer insights and analytics dashboards. They let marketers track metrics within the platform. Additionally, third-party analytics tools provide more in-depth analysis and reporting capabilities.

Tools like Google Analytics, Hootsuite, and Sprout Social help marketers gauge social media success. They offer audience segmentation , content performance tracking, and competitor analysis. This data helps businesses tweak their strategies.

• Case Studies: Highlighting Data-Driven Success

This section shows how data-driven decisions boosted social media campaigns. It features real examples. Businesses used data to improve targeting, content, and strategies. This led to better campaign results.

By studying these case studies, readers will learn how to apply data-driven decision making. Also, they will get inspired by success stories. The stories show how businesses can improve their social media campaigns. They can do this with data-driven methods.

Cross-Platform Integration

• Coordinating Campaigns Across Multiple Platforms

Businesses must be on social media to reach diverse audiences. They should run campaigns on multiple platforms. This ensures a consistent brand message and boosts social media impact. Strategies are key. They help tailor content and messages for each platform, such as Facebook or Twitter. Good coordination guarantees a strong, unified brand experience for all users.

• Consistency in Messaging and Branding

Consistency is key for cross-platform integration. It means keeping your brand’s message, tone, and look the same across all social media. This builds your brand’s identity and makes it more trusted and recognized. To achieve this, create guidelines for your brand. These should detail using the same colors, logos, fonts, and voice.

• Maximizing Reach Through Integration

Cross-platform integration boosts your reach. Each platform has unique users and strengths. For instance, Instagram is great for visuals, Twitter for updates, and LinkedIn for networking. By integrating them, you reach a wider audience. You maintain a consistent brand.

• Case Studies of Successful Cross-Platform Campaigns

To show how cross-platform integration works, let’s consider some examples. First, a clothing brand might use Instagram and Pinterest for ideas. They would use Facebook to chat with customers and Twitter for fast promos. This strategy lets the brand display products, connect with customers, and boost sales. Likewise, a tech company could use LinkedIn for business outreach, Twitter for support, and YouTube for tutorials. These examples show how businesses can blend social media well. They do this to meet goals and engage their audience.

Cross-platform integration isn’t just being on many social media platforms. It’s about creating a strong brand presence on all. This involves coordinating campaigns, staying consistent, and using each platform’s strengths. By doing so, businesses can reach more people and make social media more engaging for their audience. Case studies show the benefits. They highlight how this method can help businesses of any size or industry.

Crisis Management on Social Media

• Preparing for Potential Social Media Crises

In social media, crises can pop up without warning. So, brands must be ready. This means creating a crisis management plan. It should outline scenarios, roles, and communication strategies. First, they need to know what crises might hit. For example, these could be bad customer reviews , product recalls, or social media scandals. Next, brands can detail how to respond. Then, they choose team members and a leader. They also set up monitoring tools to catch problems quickly. This way, they can react fast.

• Handling Negative Feedback and PR Disasters

Negative social media feedback can turn into a crisis fast if not managed well. Brands need clear rules for handling negative comments or reviews. They must respond quickly and with empathy. It’s crucial to admit mistakes and offer solutions. Transparency is vital for maintaining trust during crises. For PR disasters, like product recalls or scandals, brands need a crisis communication plan. This plan should cover messaging, media responses, and a spokesperson. Effective crisis communication means providing accurate information, taking responsibility, and outlining solutions.

• Case Studies Illustrating Effective Crisis Management

To understand crisis management on social media, we should study real cases. Johnson & Johnson’s response to the 1980s Tylenol crisis is a great example. After tampered Tylenol capsules caused deaths, the company quickly recalled all products. It then openly communicated with the public. This strategy restored trust and protected the brand. Another case is Starbucks’ reaction to racial bias in a store. It apologized publicly, shut down for anti-bias training, and kept engaging with stakeholders. Both cases show the importance of being proactive in crisis management.

In today’s fast digital world, creating and running effective campaigns is crucial. This article delves into social media marketing , using real cases as examples. It covers visual content and storytelling. It also includes audience targeting, ads, influencers, analytics, and cross-platform tactics. It also includes crisis management. All are key to a successful campaign.

Social media marketing is always changing and needs flexibility. Strategies that work now might need changes later. But, with the knowledge, insights, and examples in this article, you can better manage it. Your campaigns can do more than just include posts and hashtags. They can build your brand, engage users, and meet goals. Success requires creativity, using data, and learning from others’ wins. Now, it’s time to start your journey to social media success.

• Q. What’s the importance of visual content in social media campaigns?

Visual content grabs attention and boosts engagement, making it a key element in successful campaigns.

• Q. How can I measure the success of my social media campaigns?

Define KPIs, set benchmarks, use analytics tools, and analyze data to gauge performance.

• Q. Why are real-world case studies valuable for marketers?

Case studies provide practical insights and examples of effective campaign strategies.

• Q. What role does influencer marketing play in social media campaigns?

Influencers can amplify brand messages and connect with niche audiences authentically.

• Q. How do I handle a social media crisis effectively?

Preparedness, transparency, and swift response are vital in managing social media crises.

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Social Media

The dark side of social media, a new study finds spending less time on social media leads to greater well-being..

Posted June 21, 2024 | Reviewed by Ray Parker

• A new study finds social media use is linked to increased anxiety and depression in teens.
• Social media can make teens feel worse about themselves.
• Researchers find teens who cut their social media use in half experienced less anxiety, depression, and FOMO.

In a previous post , my team and I explored how social media use can negatively impact body image in youth. As young people are on their phones more and more, constant exposure to unrealistic beauty standards can leave them particularly vulnerable to low self-esteem and unfavorable social comparisons. However, evidence suggests that poor body image is not the only impact of social media on youth.

As rates of anxiety and depression in teens have been growing alongside an increase in social media usage, we have to wonder how closely the two are connected. In 2021, Statistics Canada reported that 36% of youth experience clinically concerning symptoms of depression, and 23% experience elevated levels of anxiety. At the same time, 81.3% of Canadian youth reported spending more than two hours on social media daily, and 96% reported regular use of at least one social media platform, rates that are similar or higher among teens in the US. Multiple studies have found a correlation between social media use and poor mental health, and it makes sense why.

We all know that people tend to share just the highlights of their lives on social media, rarely sharing the challenges or low points they may be experiencing. Scrolling through social media, it seems like everyone is going on a beach holiday, showing off their perfectly airbrushed bodies, or sharing the great news of their newest accomplishments. We can't help but compare ourselves to these seemingly “perfect” lives, even when we know they are fabricated. This constant comparison can make a young person feel inadequate or worthless, leading to feelings of depression and anxiety. On top of this, the more we scroll, the more we see all the things we are missing out on. Imagine going on Instagram and noticing pictures of all your friends at a party you weren’t invited to. It hurts, right? And yet, we keep wanting to check for updates. Who is at the party? Are they having fun without me? This unhealthy cycle of fear of missing out (FoMO) can impact your self-esteem, trigger your anxiety, and make you feel incredibly alone.

In addition to negative social comparisons, displacement theory provides another answer as to why screen time and social media have a negative impact on health and mental health. The theory posits that spending large amounts of time on social media allows an individual less time to spend on other mental-health-promoting activities like sleep, physical activity, recreational and social activities with friends, and pursuing pleasurable hobbies.

Although a correlational relationship has already been established, our study is the first to examine a causal relationship between social media use and mental health in youth experiencing emotional distress. Among 220 youth experiencing symptoms of anxiety or depression, we found that reducing social media by half, to a maximum of one hour per day, led to greater reductions in anxiety, depression, the experience of FoMO, and increases in sleep compared to a placebo group that had unrestricted access. Our findings support the “displacement theory” of screen time, suggesting that spending less time on things that make people truly happy makes people more likely to experience poor mental health. Although our findings did not demonstrate that reduced social media improved mental health due to reduced negative social comparisons, it is too early to throw “the baby out with the bathwater,” as correlational studies have found this link.

While it makes sense to think that reducing social media usage would make people feel even more isolated or left out, our study indicated that the opposite was true. Although initial reduction time in social media may increase FoMO, this typically only lasts a few days, and our findings support that FoMO will go down with continued reduced use. In fact, reduced social media use may lead to increased social connection and positive mental health behaviors as people are forced to adapt and meet their social needs in healthier ways.

The study also indicated that reduced social media use led to earlier bedtimes and longer sleep. As the displacement theory suggests, less time on social media means more time to get some well-needed rest. On top of this, reduced feelings of anxiety and depression likely helped people fall asleep easier, or perhaps the increased sleep resulting from less social media use reduced anxiety and depression symptoms. Further research is needed to make the direction of these findings more clear.

The results of the study beg the question: why do we torture ourselves? Sure, social media has many benefits. It helps us connect with long-lost friends, plan our social lives, and share our successes with people we care about. But when our life becomes a constant competition , and we feel like we just don't measure up, and when we know social media takes time away from sleep and in-person social and recreational activities that make us feel good, why do we continue to use it so much?

Important takeaways from our study suggest reducing your usage of social media will help you get more sleep and boost your mood. Instead of scrolling on Instagram, try taking your dog for a walk, reading a book, or catching up with a friend. As parents, we suggest implementing rules to reduce screen time during meals or social activities to promote better attachment and connection with friends and family. We also recommend implementing a “no-phone” rule 30 minutes before bedtime and no-phones in children's and youth’s bedrooms overnight. Lastly, parents are the most important role models for their children, and there is a relationship between parent screen and social media use and their children’s mental health. This means parents should also try to reduce their own social media use and engage in non-screen health-promoting alternative activities, as well as support their children in doing the same. This will help your child promote better sleep, lead to more efficient learning at school, and improve their mental health.

Davis, C. G., & Goldfield, G. S. (2024). Limiting social media use decreases depression, anxiety, and fear of missing out in youth with emotional distress: A randomized controlled trial. Psychology of Popular Media . https://doi.org/10.1037/ppm0000536

Gary Goldfield, PhD., C. Psych., is a Senior Scientist with the Healthy Active Living & Obesity (HALO) Research Group at the Children’s Hospital of Eastern Ontario Research Institute in Ottawa, Canada.

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Are We Better Off with Less Social Media? Evidence Says Yes

Whether social media is good or bad for us remains a widely contested topic. Research shows that the same social media networks that can increase voter turnout  can also leave us feeling lonely and depressed . So how do we really know if social media’s benefits justify its costs? The results of a recent experiment published in the American Economic Review attempts to answer this question. Subjects took a “digital detox” — time spent away from social media — for a month leading up to the 2018 U.S. midterm elections. The study reveals the benefits of using free social media platforms may be lower than previously thought.

In their experiment, Alcott et al. examined the effects of a digital detox on four outcomes: self-assessments of well-being, alternative uses of time spent away from social media, political polarization, and post-detox behavior. They recruited 2,743 users through Facebook advertisements, offering $30 gift cards in exchange for consent to share public profiles. According to the authors, this sample was “young, well-educated, and left-leaning compared to the average Facebook user.” They caution that their study recruits may have differed from each other in ways they could not observe, which could lead to endogeneity.

In addition to making participants deactivate their Facebook accounts for 24 hours, the researchers made a price offer to determine how much money participants would accept in exchange for deactivating Facebook for a whole month. They settled on $102, which participants in the treatment group received to deactivate Facebook. They randomly assigned 61% of the sample population to either this treatment group, or a control group that received no money. The authors then analyzed the effects of the digital detox by regularly administering surveys, tracking activity on social media profiles, and checking link-clicking behavior through emails.

From the survey data, Alcott et al. found significant improvements in self-reported measures of well-being, such as happiness, life-satisfaction, and anxiety among those who participated in the detox. 80% of participants shared that “deactivating Facebook was good for them” after the study ended.

Deactivating Facebook also increased offline activity. An average of 60 minutes per day was freed up for the detox participants, who reported spending more offline time with friends, family, and themselves. Interestingly, less time on Facebook translated into less time spent on other online activities as well. Alcott et al. find that while Facebook is a substitute for offline activities, it is a complement to other online activities.

Deactivating Facebook reduced factual news knowledge, but it also reduced political polarization. The most significant effect of deactivation was reduced exposure to news that reportedly made participants “better understand the view of their own political party relative to the other party.” Alcott et al. find that deactivation moves both Democrats and Republicans closer to the center, confirming prior findings that social media users are generally exposed to content that is ideologically congenial — especially on Facebook . While social media can enhance democracy by increasing awareness of current events, it can also create political polarization.

Finally, post-detox outcomes support the hypothesis that social media usage is habit-forming. Several weeks after the experiment ended, detox participants were using Facebook 22% less than the control group. While only 5% kept their accounts deactivated, most participants reported an intention to reduce Facebook usage. Tracking link-clicking on emails about ways to reduce social media consumption and engage in political causes provided even more support for Alcott et al. to conclude that “at least some learning or ‘detox’ from addiction” happened through deactivation.

Alcott et al. then calculated the net benefits of social media using both a traditional approach to welfare analysis and an updated approach that factored in their results. The traditional approach assumes that consumers are rational — their choices reflect the best possible way they could spend their money and time. Under this framework, the authors estimate that four weeks of Facebook usage creates $31 billion in consumer surplus for 172 million Facebook users.

But in reality, consumers are not fully aware of the consequences of their consumption choices. Alcott et al. use their findings to quantify how a detox changes what a consumer believes is optimal. They find that after the four-week detox, the amount of money users accepted for deactivating Facebook decreased by 14%, lowering the “benefit” side of the cost-benefit trade-off. This suggests that traditional approaches to welfare analysis are likely overestimating the value consumers place on social media.

Increasing awareness of social media’s consumption drivers and consequences — as well as detox practices — could potentially shift individual behavior towards improved well-being and less time on social media. This experiment is by no means the final word on whether the benefits outweigh the costs of social media consumption. Future research will need to better account for how social media’s increasing share in the global economy contributes to overall consumer surplus.

Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. 2020. The Welfare Effects of Social Media. American Economic Review 110: 629-676. doi.org/10.1257/aer.20190658 .

Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. 2012. “A 61-million-person experiment in social influence and political mobilization.” Nature 489: 295-298. doi.org/10.1038/nature11421 .

Hunt, M. G., Marx, R., Lipson, C., & Young, J. 2018. “No More FOMO: Limiting Social Media Decreases Loneliness and Depression.” Journal of Social and Clinical Psychology 37: 751-768. doi.org/10.1521/jscp.2018.37.10.751 .

Gentzkow, M., & Shapiro, J. M. 2011. “Ideological Segregation Online and Offline.” The Quarterly Journal of Economics 126: 1799-1839. doi.org/10.1093/qje/qjr044 .

Bakshy, E., Messing, S., & Adamic, L. A. 2015. “Exposure to ideologically diverse news and opinion on Facebook.” Science 348: 1130-1132. doi.org/10.1126/science.aaa1160 .

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The impact of social media on social lifestyle: A case study of university female students

• December 2017
• 15(4):9966 - 9981

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JSmol Viewer

Detecting fake accounts on social media portals—the x portal case study.

1. Introduction

• Matrimonial fraud;
• Phishing (impersonating another person or institution to obtain essential data);
• Hacking (e.g., breaking into a user’s computer and taking control of it);
• Cyberstalking (online harassment).
• Present a distinctive visual-based approach to account classification;
• Create an image dataset of platform X accounts;
• Validate the created dataset;
• Test the detection of the authenticity of an X portal account by using the selected machine learning model.

2. Related Works

3. creating a dataset of twitter accounts, 3.1. definition of various types of accounts, 3.2. feature engineering to generate a dataset, 3.3. types and characteristics of generated accounts on x portal, 3.4. generation and presentation of accounts’ images, 4. experiments and results, 4.1. machine learning model selection and optimization, 4.2. detection of fake accounts.

• Upon first launching the extension, the user navigates to the X social network profile of their interest ( Figure 11 a);
• Within the extension, the user selects the button to detect if the account is fake ( Figure 11 b);
• The extension takes a screenshot of the web page element containing the profile on the portal;
• The image is sent to the facade component by the WebSocket API;
• The facade forwards the image as the input to the machine learning model;
• The model predicts whether the analyzed account is fake and returns the results;
• Through the facade, the results are sent back to the end user ( Figure 11 c);
• The probability of the account being fake is displayed to the user in the extension interface.

4.3. Tests Performed on Fake Account Detection Tool

4.3.1. testing in implemented copy of x environment, 4.3.2. testing in original x environment, 5. discussion, 6. conclusions, author contributions, data availability statement, conflicts of interest.

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Share and Cite

Dracewicz, W.; Sepczuk, M. Detecting Fake Accounts on Social Media Portals—The X Portal Case Study. Electronics 2024 , 13 , 2542. https://doi.org/10.3390/electronics13132542

Dracewicz W, Sepczuk M. Detecting Fake Accounts on Social Media Portals—The X Portal Case Study. Electronics . 2024; 13(13):2542. https://doi.org/10.3390/electronics13132542

Dracewicz, Weronika, and Mariusz Sepczuk. 2024. "Detecting Fake Accounts on Social Media Portals—The X Portal Case Study" Electronics 13, no. 13: 2542. https://doi.org/10.3390/electronics13132542

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Home > College of Humanities and Social Sciences > Government and International Affairs > MPA Practicums > 2

Master of Public Administration Practicums

Effective social media use by law enforcement agencies: a case study approach to quantifying and improving efficacy and developing agency best practices.

David T. Snively , Kennesaw State University Follow

Date of Completion

Degree type, degree name.

Master of Public Administration (MPA)

Public Administration

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Governmental Administration

Committee Chair/First Advisor

Dr. Jerry Herbel

In the wake of protests against law enforcement for an array of reasons, law enforcement officers and agencies have a responsibility to recognize and utilize the available mediums of communication with which they may best develop a connection to the communities they serve. Furthermore, law enforcement agencies must be informed that established, traditional methods of news dissemination – such as press conferences and printed articles – are now both ineffective and under-utilized, replaced in large part by social media live-time reports. For that reason, law enforcement agency executives must address both the responsibility to provide appropriately timed updates to critical incidents and events, and utilize the opportunity to engage their community through a widely utilized and accepted medium of communication. Both require an understanding of social media, its application, and its efficacy both literal and perceived.

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Fake news, disinformation and misinformation in social media: a review

Esma aïmeur.

Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada

Sabrine Amri

Gilles brassard, associated data.

All the data and material are available in the papers cited in the references.

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Introduction

Context and motivation.

Fake news, disinformation and misinformation have become such a scourge that Marcia McNutt, president of the National Academy of Sciences of the United States, is quoted to have said (making an implicit reference to the COVID-19 pandemic) “Misinformation is worse than an epidemic: It spreads at the speed of light throughout the globe and can prove deadly when it reinforces misplaced personal bias against all trustworthy evidence” in a joint statement of the National Academies 1 posted on July 15, 2021. Indeed, although online social networks (OSNs), also called social media, have improved the ease with which real-time information is broadcast; its popularity and its massive use have expanded the spread of fake news by increasing the speed and scope at which it can spread. Fake news may refer to the manipulation of information that can be carried out through the production of false information, or the distortion of true information. However, that does not mean that this problem is only created with social media. A long time ago, there were rumors in the traditional media that Elvis was not dead, 2 that the Earth was flat, 3 that aliens had invaded us, 4 , etc.

Therefore, social media has become nowadays a powerful source for fake news dissemination (Sharma et al. 2019 ; Shu et al. 2017 ). According to Pew Research Center’s analysis of the news use across social media platforms, in 2020, about half of American adults get news on social media at least sometimes, 5 while in 2018, only one-fifth of them say they often get news via social media. 6

Hence, fake news can have a significant impact on society as manipulated and false content is easier to generate and harder to detect (Kumar and Shah 2018 ) and as disinformation actors change their tactics (Kumar and Shah 2018 ; Micallef et al. 2020 ). In 2017, Snow predicted in the MIT Technology Review (Snow 2017 ) that most individuals in mature economies will consume more false than valid information by 2022.

Recent news on the COVID-19 pandemic, which has flooded the web and created panic in many countries, has been reported as fake. 7 For example, holding your breath for ten seconds to one minute is not a self-test for COVID-19 8 (see Fig.  1 ). Similarly, online posts claiming to reveal various “cures” for COVID-19 such as eating boiled garlic or drinking chlorine dioxide (which is an industrial bleach), were verified 9 as fake and in some cases as dangerous and will never cure the infection.

Fake news example about a self-test for COVID-19 source: https://cdn.factcheck.org/UploadedFiles/Screenshot031120_false.jpg , last access date: 26-12-2022

Social media outperformed television as the major news source for young people of the UK and the USA. 10 Moreover, as it is easier to generate and disseminate news online than with traditional media or face to face, large volumes of fake news are produced online for many reasons (Shu et al. 2017 ). Furthermore, it has been reported in a previous study about the spread of online news on Twitter (Vosoughi et al. 2018 ) that the spread of false news online is six times faster than truthful content and that 70% of the users could not distinguish real from fake news (Vosoughi et al. 2018 ) due to the attraction of the novelty of the latter (Bovet and Makse 2019 ). It was determined that falsehood spreads significantly farther, faster, deeper and more broadly than the truth in all categories of information, and the effects are more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information (Vosoughi et al. 2018 ).

Over 1 million tweets were estimated to be related to fake news by the end of the 2016 US presidential election. 11 In 2017, in Germany, a government spokesman affirmed: “We are dealing with a phenomenon of a dimension that we have not seen before,” referring to an unprecedented spread of fake news on social networks. 12 Given the strength of this new phenomenon, fake news has been chosen as the word of the year by the Macquarie dictionary both in 2016 13 and in 2018 14 as well as by the Collins dictionary in 2017. 15 , 16 Since 2020, the new term “infodemic” was coined, reflecting widespread researchers’ concern (Gupta et al. 2022 ; Apuke and Omar 2021 ; Sharma et al. 2020 ; Hartley and Vu 2020 ; Micallef et al. 2020 ) about the proliferation of misinformation linked to the COVID-19 pandemic.

The Gartner Group’s top strategic predictions for 2018 and beyond included the need for IT leaders to quickly develop Artificial Intelligence (AI) algorithms to address counterfeit reality and fake news. 17 However, fake news identification is a complex issue. (Snow 2017 ) questioned the ability of AI to win the war against fake news. Similarly, other researchers concurred that even the best AI for spotting fake news is still ineffective. 18 Besides, recent studies have shown that the power of AI algorithms for identifying fake news is lower than its ability to create it Paschen ( 2019 ). Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed to closely resemble the truth in order to deceive users, and as a result, it is often hard to determine its veracity by AI alone. Therefore, it is crucial to consider more effective approaches to solve the problem of fake news in social media.

Contribution

The fake news problem has been addressed by researchers from various perspectives related to different topics. These topics include, but are not restricted to, social science studies , which investigate why and who falls for fake news (Altay et al. 2022 ; Batailler et al. 2022 ; Sterret et al. 2018 ; Badawy et al. 2019 ; Pennycook and Rand 2020 ; Weiss et al. 2020 ; Guadagno and Guttieri 2021 ), whom to trust and how perceptions of misinformation and disinformation relate to media trust and media consumption patterns (Hameleers et al. 2022 ), how fake news differs from personal lies (Chiu and Oh 2021 ; Escolà-Gascón 2021 ), examine how can the law regulate digital disinformation and how governments can regulate the values of social media companies that themselves regulate disinformation spread on their platforms (Marsden et al. 2020 ; Schuyler 2019 ; Vasu et al. 2018 ; Burshtein 2017 ; Waldman 2017 ; Alemanno 2018 ; Verstraete et al. 2017 ), and argue the challenges to democracy (Jungherr and Schroeder 2021 ); Behavioral interventions studies , which examine what literacy ideas mean in the age of dis/mis- and malinformation (Carmi et al. 2020 ), investigate whether media literacy helps identification of fake news (Jones-Jang et al. 2021 ) and attempt to improve people’s news literacy (Apuke et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers 2022 ; Nagel 2022 ; Jones-Jang et al. 2021 ; Mihailidis and Viotty 2017 ; García et al. 2020 ) by encouraging people to pause to assess credibility of headlines (Fazio 2020 ), promote civic online reasoning (McGrew 2020 ; McGrew et al. 2018 ) and critical thinking (Lutzke et al. 2019 ), together with evaluations of credibility indicators (Bhuiyan et al. 2020 ; Nygren et al. 2019 ; Shao et al. 2018a ; Pennycook et al. 2020a , b ; Clayton et al. 2020 ; Ozturk et al. 2015 ; Metzger et al. 2020 ; Sherman et al. 2020 ; Nekmat 2020 ; Brashier et al. 2021 ; Chung and Kim 2021 ; Lanius et al. 2021 ); as well as social media-driven studies , which investigate the effect of signals (e.g., sources) to detect and recognize fake news (Vraga and Bode 2017 ; Jakesch et al. 2019 ; Shen et al. 2019 ; Avram et al. 2020 ; Hameleers et al. 2020 ; Dias et al. 2020 ; Nyhan et al. 2020 ; Bode and Vraga 2015 ; Tsang 2020 ; Vishwakarma et al. 2019 ; Yavary et al. 2020 ) and investigate fake and reliable news sources using complex networks analysis based on search engine optimization metric (Mazzeo and Rapisarda 2022 ).

The impacts of fake news have reached various areas and disciplines beyond online social networks and society (García et al. 2020 ) such as economics (Clarke et al. 2020 ; Kogan et al. 2019 ; Goldstein and Yang 2019 ), psychology (Roozenbeek et al. 2020a ; Van der Linden and Roozenbeek 2020 ; Roozenbeek and van der Linden 2019 ), political science (Valenzuela et al. 2022 ; Bringula et al. 2022 ; Ricard and Medeiros 2020 ; Van der Linden et al. 2020 ; Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ), health science (Alonso-Galbán and Alemañy-Castilla 2022 ; Desai et al. 2022 ; Apuke and Omar 2021 ; Escolà-Gascón 2021 ; Wang et al. 2019c ; Hartley and Vu 2020 ; Micallef et al. 2020 ; Pennycook et al. 2020b ; Sharma et al. 2020 ; Roozenbeek et al. 2020b ), environmental science (e.g., climate change) (Treen et al. 2020 ; Lutzke et al. 2019 ; Lewandowsky 2020 ; Maertens et al. 2020 ), etc.

Interesting research has been carried out to review and study the fake news issue in online social networks. Some focus not only on fake news, but also distinguish between fake news and rumor (Bondielli and Marcelloni 2019 ; Meel and Vishwakarma 2020 ), while others tackle the whole problem, from characterization to processing techniques (Shu et al. 2017 ; Guo et al. 2020 ; Zhou and Zafarani 2020 ). However, they mostly focus on studying approaches from a machine learning perspective (Bondielli and Marcelloni 2019 ), data mining perspective (Shu et al. 2017 ), crowd intelligence perspective (Guo et al. 2020 ), or knowledge-based perspective (Zhou and Zafarani 2020 ). Furthermore, most of these studies ignore at least one of the mentioned perspectives, and in many cases, they do not cover other existing detection approaches using methods such as blockchain and fact-checking, as well as analysis on metrics used for Search Engine Optimization (Mazzeo and Rapisarda 2022 ). However, in our work and to the best of our knowledge, we cover all the approaches used for fake news detection. Indeed, we investigate the proposed solutions from broader perspectives (i.e., the detection techniques that are used, as well as the different aspects and types of the information used).

Therefore, in this paper, we are highly motivated by the following facts. First, fake news detection on social media is still in the early age of development, and many challenging issues remain that require deeper investigation. Hence, it is necessary to discuss potential research directions that can improve fake news detection and mitigation tasks. However, the dynamic nature of fake news propagation through social networks further complicates matters (Sharma et al. 2019 ). False information can easily reach and impact a large number of users in a short time (Friggeri et al. 2014 ; Qian et al. 2018 ). Moreover, fact-checking organizations cannot keep up with the dynamics of propagation as they require human verification, which can hold back a timely and cost-effective response (Kim et al. 2018 ; Ruchansky et al. 2017 ; Shu et al. 2018a ).

Our work focuses primarily on understanding the “fake news” problem, its related challenges and root causes, and reviewing automatic fake news detection and mitigation methods in online social networks as addressed by researchers. The main contributions that differentiate us from other works are summarized below:

• We present the general context from which the fake news problem emerged (i.e., online deception)
• We review existing definitions of fake news, identify the terms and features most commonly used to define fake news, and categorize related works accordingly.
• We propose a fake news typology classification based on the various categorizations of fake news reported in the literature.
• We point out the most challenging factors preventing researchers from proposing highly effective solutions for automatic fake news detection in social media.
• We highlight and classify representative studies in the domain of automatic fake news detection and mitigation on online social networks including the key methods and techniques used to generate detection models.
• We discuss the key shortcomings that may inhibit the effectiveness of the proposed fake news detection methods in online social networks.
• We provide recommendations that can help address these shortcomings and improve the quality of research in this domain.

The rest of this article is organized as follows. We explain the methodology with which the studied references are collected and selected in Sect.  2 . We introduce the online deception problem in Sect.  3 . We highlight the modern-day problem of fake news in Sect.  4 , followed by challenges facing fake news detection and mitigation tasks in Sect.  5 . We provide a comprehensive literature review of the most relevant scholarly works on fake news detection in Sect.  6 . We provide a critical discussion and recommendations that may fill some of the gaps we have identified, as well as a classification of the reviewed automatic fake news detection approaches, in Sect.  7 . Finally, we provide a conclusion and propose some future directions in Sect.  8 .

Review methodology

This section introduces the systematic review methodology on which we relied to perform our study. We start with the formulation of the research questions, which allowed us to select the relevant research literature. Then, we provide the different sources of information together with the search and inclusion/exclusion criteria we used to select the final set of papers.

Research questions formulation

The research scope, research questions, and inclusion/exclusion criteria were established following an initial evaluation of the literature and the following research questions were formulated and addressed.

• RQ1: what is fake news in social media, how is it defined in the literature, what are its related concepts, and the different types of it?
• RQ2: What are the existing challenges and issues related to fake news?
• RQ3: What are the available techniques used to perform fake news detection in social media?

Sources of information

We broadly searched for journal and conference research articles, books, and magazines as a source of data to extract relevant articles. We used the main sources of scientific databases and digital libraries in our search, such as Google Scholar, 19 IEEE Xplore, 20 Springer Link, 21 ScienceDirect, 22 Scopus, 23 ACM Digital Library. 24 Also, we screened most of the related high-profile conferences such as WWW, SIGKDD, VLDB, ICDE and so on to find out the recent work.

Search criteria

We focused our research over a period of ten years, but we made sure that about two-thirds of the research papers that we considered were published in or after 2019. Additionally, we defined a set of keywords to search the above-mentioned scientific databases since we concentrated on reviewing the current state of the art in addition to the challenges and the future direction. The set of keywords includes the following terms: fake news, disinformation, misinformation, information disorder, social media, detection techniques, detection methods, survey, literature review.

Study selection, exclusion and inclusion criteria

To retrieve relevant research articles, based on our sources of information and search criteria, a systematic keyword-based search was carried out by posing different search queries, as shown in Table  1 .

List of keywords for searching relevant articles

We discovered a primary list of articles. On the obtained initial list of studies, we applied a set of inclusion/exclusion criteria presented in Table  2 to select the appropriate research papers. The inclusion and exclusion principles are applied to determine whether a study should be included or not.

Inclusion and exclusion criteria

After reading the abstract, we excluded some articles that did not meet our criteria. We chose the most important research to help us understand the field. We reviewed the articles completely and found only 61 research papers that discuss the definition of the term fake news and its related concepts (see Table  4 ). We used the remaining papers to understand the field, reveal the challenges, review the detection techniques, and discuss future directions.

Classification of fake news definitions based on the used term and features

A brief introduction of online deception

The Cambridge Online Dictionary defines Deception as “ the act of hiding the truth, especially to get an advantage .” Deception relies on peoples’ trust, doubt and strong emotions that may prevent them from thinking and acting clearly (Aïmeur et al. 2018 ). We also define it in previous work (Aïmeur et al. 2018 ) as the process that undermines the ability to consciously make decisions and take convenient actions, following personal values and boundaries. In other words, deception gets people to do things they would not otherwise do. In the context of online deception, several factors need to be considered: the deceiver, the purpose or aim of the deception, the social media service, the deception technique and the potential target (Aïmeur et al. 2018 ; Hage et al. 2021 ).

Researchers are working on developing new ways to protect users and prevent online deception (Aïmeur et al. 2018 ). Due to the sophistication of attacks, this is a complex task. Hence, malicious attackers are using more complex tools and strategies to deceive users. Furthermore, the way information is organized and exchanged in social media may lead to exposing OSN users to many risks (Aïmeur et al. 2013 ).

In fact, this field is one of the recent research areas that need collaborative efforts of multidisciplinary practices such as psychology, sociology, journalism, computer science as well as cyber-security and digital marketing (which are not yet well explored in the field of dis/mis/malinformation but relevant for future research). Moreover, Ismailov et al. ( 2020 ) analyzed the main causes that could be responsible for the efficiency gap between laboratory results and real-world implementations.

In this paper, it is not in our scope of work to review online deception state of the art. However, we think it is crucial to note that fake news, misinformation and disinformation are indeed parts of the larger landscape of online deception (Hage et al. 2021 ).

Fake news, the modern-day problem

Fake news has existed for a very long time, much before their wide circulation became facilitated by the invention of the printing press. 25 For instance, Socrates was condemned to death more than twenty-five hundred years ago under the fake news that he was guilty of impiety against the pantheon of Athens and corruption of the youth. 26 A Google Trends Analysis of the term “fake news” reveals an explosion in popularity around the time of the 2016 US presidential election. 27 Fake news detection is a problem that has recently been addressed by numerous organizations, including the European Union 28 and NATO. 29

In this section, we first overview the fake news definitions as they were provided in the literature. We identify the terms and features used in the definitions, and we classify the latter based on them. Then, we provide a fake news typology based on distinct categorizations that we propose, and we define and compare the most cited forms of one specific fake news category (i.e., the intent-based fake news category).

Definitions of fake news

“Fake news” is defined in the Collins English Dictionary as false and often sensational information disseminated under the guise of news reporting, 30 yet the term has evolved over time and has become synonymous with the spread of false information (Cooke 2017 ).

The first definition of the term fake news was provided by Allcott and Gentzkow ( 2017 ) as news articles that are intentionally and verifiably false and could mislead readers. Then, other definitions were provided in the literature, but they all agree on the authenticity of fake news to be false (i.e., being non-factual). However, they disagree on the inclusion and exclusion of some related concepts such as satire , rumors , conspiracy theories , misinformation and hoaxes from the given definition. More recently, Nakov ( 2020 ) reported that the term fake news started to mean different things to different people, and for some politicians, it even means “news that I do not like.”

Hence, there is still no agreed definition of the term “fake news.” Moreover, we can find many terms and concepts in the literature that refer to fake news (Van der Linden et al. 2020 ; Molina et al. 2021 ) (Abu Arqoub et al. 2022 ; Allen et al. 2020 ; Allcott and Gentzkow 2017 ; Shu et al. 2017 ; Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Conroy et al. 2015 ; Celliers and Hattingh 2020 ; Nakov 2020 ; Shu et al. 2020c ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ; Egelhofer and Lecheler 2019 ; Mustafaraj and Metaxas 2017 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Lazer et al. 2018 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ), disinformation (Kapantai et al. 2021 ; Shu et al. 2020a , c ; Kumar et al. 2016 ; Bhattacharjee et al. 2020 ; Marsden et al. 2020 ; Jungherr and Schroeder 2021 ; Starbird et al. 2019 ; Ireton and Posetti 2018 ), misinformation (Wu et al. 2019 ; Shu et al. 2020c ; Shao et al. 2016 , 2018b ; Pennycook and Rand 2019 ; Micallef et al. 2020 ), malinformation (Dame Adjin-Tettey 2022 ) (Carmi et al. 2020 ; Shu et al. 2020c ), false information (Kumar and Shah 2018 ; Guo et al. 2020 ; Habib et al. 2019 ), information disorder (Shu et al. 2020c ; Wardle and Derakhshan 2017 ; Wardle 2018 ; Derakhshan and Wardle 2017 ), information warfare (Guadagno and Guttieri 2021 ) and information pollution (Meel and Vishwakarma 2020 ).

There is also a remarkable amount of disagreement over the classification of the term fake news in the research literature, as well as in policy (de Cock Buning 2018 ; ERGA 2018 , 2021 ). Some consider fake news as a type of misinformation (Allen et al. 2020 ; Singh et al. 2021 ; Ha et al. 2021 ; Pennycook and Rand 2019 ; Shao et al. 2018b ; Di Domenico et al. 2021 ; Sharma et al. 2019 ; Celliers and Hattingh 2020 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Islam et al. 2020 ), others consider it as a type of disinformation (de Cock Buning 2018 ) (Bringula et al. 2022 ; Baptista and Gradim 2022 ; Tsang 2020 ; Tandoc Jr et al. 2021 ; Bastick 2021 ; Khan et al. 2019 ; Shu et al. 2017 ; Nakov 2020 ; Shu et al. 2020c ; Egelhofer and Lecheler 2019 ), while others associate the term with both disinformation and misinformation (Wu et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers et al. 2022 ; Carmi et al. 2020 ; Allcott and Gentzkow 2017 ; Zhang and Ghorbani 2020 ; Potthast et al. 2017 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ). On the other hand, some prefer to differentiate fake news from both terms (ERGA 2018 ; Molina et al. 2021 ; ERGA 2021 ) (Zhou and Zafarani 2020 ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ).

The existing terms can be separated into two groups. The first group represents the general terms, which are information disorder , false information and fake news , each of which includes a subset of terms from the second group. The second group represents the elementary terms, which are misinformation , disinformation and malinformation . The literature agrees on the definitions of the latter group, but there is still no agreed-upon definition of the first group. In Fig.  2 , we model the relationship between the most used terms in the literature.

Modeling of the relationship between terms related to fake news

The terms most used in the literature to refer, categorize and classify fake news can be summarized and defined as shown in Table  3 , in which we capture the similarities and show the differences between the different terms based on two common key features, which are the intent and the authenticity of the news content. The intent feature refers to the intention behind the term that is used (i.e., whether or not the purpose is to mislead or cause harm), whereas the authenticity feature refers to its factual aspect. (i.e., whether the content is verifiably false or not, which we label as genuine in the second case). Some of these terms are explicitly used to refer to fake news (i.e., disinformation, misinformation and false information), while others are not (i.e., malinformation). In the comparison table, the empty dash (–) cell denotes that the classification does not apply.

A comparison between used terms based on intent and authenticity

In Fig.  3 , we identify the different features used in the literature to define fake news (i.e., intent, authenticity and knowledge). Hence, some definitions are based on two key features, which are authenticity and intent (i.e., news articles that are intentionally and verifiably false and could mislead readers). However, other definitions are based on either authenticity or intent. Other researchers categorize false information on the web and social media based on its intent and knowledge (i.e., when there is a single ground truth). In Table  4 , we classify the existing fake news definitions based on the used term and the used features . In the classification, the references in the cells refer to the research study in which a fake news definition was provided, while the empty dash (–) cells denote that the classification does not apply.

The features used for fake news definition

Fake news typology

Various categorizations of fake news have been provided in the literature. We can distinguish two major categories of fake news based on the studied perspective (i.e., intention or content) as shown in Fig.  4 . However, our proposed fake news typology is not about detection methods, and it is not exclusive. Hence, a given category of fake news can be described based on both perspectives (i.e., intention and content) at the same time. For instance, satire (i.e., intent-based fake news) can contain text and/or multimedia content types of data (e.g., headline, body, image, video) (i.e., content-based fake news) and so on.

Most researchers classify fake news based on the intent (Collins et al. 2020 ; Bondielli and Marcelloni 2019 ; Zannettou et al. 2019 ; Kumar et al. 2016 ; Wardle 2017 ; Shu et al. 2017 ; Kumar and Shah 2018 ) (see Sect.  4.2.2 ). However, other researchers (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ) focus on the content to categorize types of fake news through distinguishing the different formats and content types of data in the news (e.g., text and/or multimedia).

Recently, another classification was proposed by Zhang and Ghorbani ( 2020 ). It is based on the combination of content and intent to categorize fake news. They distinguish physical news content and non-physical news content from fake news. Physical content consists of the carriers and format of the news, and non-physical content consists of the opinions, emotions, attitudes and sentiments that the news creators want to express.

Content-based fake news category

According to researchers of this category (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ), forms of fake news may include false text such as hyperlinks or embedded content; multimedia such as false videos (Demuyakor and Opata 2022 ), images (Masciari et al. 2020 ; Shen et al. 2019 ), audios (Demuyakor and Opata 2022 ) and so on. Moreover, we can also find multimodal content (Shu et al. 2020a ) that is fake news articles and posts composed of multiple types of data combined together, for example, a fabricated image along with a text related to the image (Shu et al. 2020a ). In this category of fake news forms, we can mention as examples deepfake videos (Yang et al. 2019b ) and GAN-generated fake images (Zhang et al. 2019b ), which are artificial intelligence-based machine-generated fake content that are hard for unsophisticated social network users to identify.

The effects of these forms of fake news content vary on the credibility assessment, as well as sharing intentions which influences the spread of fake news on OSNs. For instance, people with little knowledge about the issue compared to those who are strongly concerned about the key issue of fake news tend to be easier to convince that the misleading or fake news is real, especially when shared via a video modality as compared to the text or the audio modality (Demuyakor and Opata 2022 ).

Intent-based Fake News Category

The most often mentioned and discussed forms of fake news according to researchers in this category include but are not restricted to clickbait , hoax , rumor , satire , propaganda , framing , conspiracy theories and others. In the following subsections, we explain these types of fake news as they were defined in the literature and undertake a brief comparison between them as depicted in Table  5 . The following are the most cited forms of intent-based types of fake news, and their comparison is based on what we suspect are the most common criteria mentioned by researchers.

A comparison between the different types of intent-based fake news

Clickbait refers to misleading headlines and thumbnails of content on the web (Zannettou et al. 2019 ) that tend to be fake stories with catchy headlines aimed at enticing the reader to click on a link (Collins et al. 2020 ). This type of fake news is considered to be the least severe type of false information because if a user reads/views the whole content, it is possible to distinguish if the headline and/or the thumbnail was misleading (Zannettou et al. 2019 ). However, the goal behind using clickbait is to increase the traffic to a website (Zannettou et al. 2019 ).

A hoax is a false (Zubiaga et al. 2018 ) or inaccurate (Zannettou et al. 2019 ) intentionally fabricated (Collins et al. 2020 ) news story used to masquerade the truth (Zubiaga et al. 2018 ) and is presented as factual (Zannettou et al. 2019 ) to deceive the public or audiences (Collins et al. 2020 ). This category is also known either as half-truth or factoid stories (Zannettou et al. 2019 ). Popular examples of hoaxes are stories that report the false death of celebrities (Zannettou et al. 2019 ) and public figures (Collins et al. 2020 ). Recently, hoaxes about the COVID-19 have been circulating through social media.

The term rumor refers to ambiguous or never confirmed claims (Zannettou et al. 2019 ) that are disseminated with a lack of evidence to support them (Sharma et al. 2019 ). This kind of information is widely propagated on OSNs (Zannettou et al. 2019 ). However, they are not necessarily false and may turn out to be true (Zubiaga et al. 2018 ). Rumors originate from unverified sources but may be true or false or remain unresolved (Zubiaga et al. 2018 ).

Satire refers to stories that contain a lot of irony and humor (Zannettou et al. 2019 ). It presents stories as news that might be factually incorrect, but the intent is not to deceive but rather to call out, ridicule, or to expose behavior that is shameful, corrupt, or otherwise “bad” (Golbeck et al. 2018 ). This is done with a fabricated story or by exaggerating the truth reported in mainstream media in the form of comedy (Collins et al. 2020 ). The intent behind satire seems kind of legitimate and many authors (such as Wardle (Wardle 2017 )) do include satire as a type of fake news as there is no intention to cause harm but it has the potential to mislead or fool people.

Also, Golbeck et al. ( 2018 ) mention that there is a spectrum from fake to satirical news that they found to be exploited by many fake news sites. These sites used disclaimers at the bottom of their webpages to suggest they were “satirical” even when there was nothing satirical about their articles, to protect them from accusations about being fake. The difference with a satirical form of fake news is that the authors or the host present themselves as a comedian or as an entertainer rather than a journalist informing the public (Collins et al. 2020 ). However, most audiences believed the information passed in this satirical form because the comedian usually projects news from mainstream media and frames them to suit their program (Collins et al. 2020 ).

Propaganda refers to news stories created by political entities to mislead people. It is a special instance of fabricated stories that aim to harm the interests of a particular party and, typically, has a political context (Zannettou et al. 2019 ). Propaganda was widely used during both World Wars (Collins et al. 2020 ) and during the Cold War (Zannettou et al. 2019 ). It is a consequential type of false information as it can change the course of human history (e.g., by changing the outcome of an election) (Zannettou et al. 2019 ). States are the main actors of propaganda. Recently, propaganda has been used by politicians and media organizations to support a certain position or view (Collins et al. 2020 ). Online astroturfing can be an example of the tools used for the dissemination of propaganda. It is a covert manipulation of public opinion (Peng et al. 2017 ) that aims to make it seem that many people share the same opinion about something. Astroturfing can affect different domains of interest, based on which online astroturfing can be mainly divided into political astroturfing, corporate astroturfing and astroturfing in e-commerce or online services (Mahbub et al. 2019 ). Propaganda types of fake news can be debunked with manual fact-based detection models such as the use of expert-based fact-checkers (Collins et al. 2020 ).

Framing refers to employing some aspect of reality to make content more visible, while the truth is concealed (Collins et al. 2020 ) to deceive and misguide readers. People will understand certain concepts based on the way they are coined and invented. An example of framing was provided by Collins et al. ( 2020 ): “suppose a leader X says “I will neutralize my opponent” simply meaning he will beat his opponent in a given election. Such a statement will be framed such as “leader X threatens to kill Y” and this framed statement provides a total misrepresentation of the original meaning.

Conspiracy Theories

Conspiracy theories refer to the belief that an event is the result of secret plots generated by powerful conspirators. Conspiracy belief refers to people’s adoption and belief of conspiracy theories, and it is associated with psychological, political and social factors (Douglas et al. 2019 ). Conspiracy theories are widespread in contemporary democracies (Sutton and Douglas 2020 ), and they have major consequences. For instance, lately and during the COVID-19 pandemic, conspiracy theories have been discussed from a public health perspective (Meese et al. 2020 ; Allington et al. 2020 ; Freeman et al. 2020 ).

Comparison Between Most Popular Intent-based Types of Fake News

Following a review of the most popular intent-based types of fake news, we compare them as shown in Table  5 based on the most common criteria mentioned by researchers in their definitions as listed below.

• the intent behind the news, which refers to whether a given news type was mainly created to intentionally deceive people or not (e.g., humor, irony, entertainment, etc.);
• the way that the news propagates through OSN, which determines the nature of the propagation of each type of fake news and this can be either fast or slow propagation;
• the severity of the impact of the news on OSN users, which refers to whether the public has been highly impacted by the given type of fake news; the mentioned impact of each fake news type is mainly the proportion of the negative impact;
• and the goal behind disseminating the news, which can be to gain popularity for a particular entity (e.g., political party), for profit (e.g., lucrative business), or other reasons such as humor and irony in the case of satire, spreading panic or anger, and manipulating the public in the case of hoaxes, made-up stories about a particular person or entity in the case of rumors, and misguiding readers in the case of framing.

However, the comparison provided in Table  5 is deduced from the studied research papers; it is our point of view, which is not based on empirical data.

We suspect that the most dangerous types of fake news are the ones with high intention to deceive the public, fast propagation through social media, high negative impact on OSN users, and complicated hidden goals and agendas. However, while the other types of fake news are less dangerous, they should not be ignored.

Moreover, it is important to highlight that the existence of the overlap in the types of fake news mentioned above has been proven, thus it is possible to observe false information that may fall within multiple categories (Zannettou et al. 2019 ). Here, we provide two examples by Zannettou et al. ( 2019 ) to better understand possible overlaps: (1) a rumor may also use clickbait techniques to increase the audience that will read the story; and (2) propaganda stories, as a special instance of a framing story.

Challenges related to fake news detection and mitigation

To alleviate fake news and its threats, it is crucial to first identify and understand the factors involved that continue to challenge researchers. Thus, the main question is to explore and investigate the factors that make it easier to fall for manipulated information. Despite the tremendous progress made in alleviating some of the challenges in fake news detection (Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Shu et al. 2020a ), much more work needs to be accomplished to address the problem effectively.

In this section, we discuss several open issues that have been making fake news detection in social media a challenging problem. These issues can be summarized as follows: content-based issues (i.e., deceptive content that resembles the truth very closely), contextual issues (i.e., lack of user awareness, social bots spreaders of fake content, and OSN’s dynamic natures that leads to the fast propagation), as well as the issue of existing datasets (i.e., there still no one size fits all benchmark dataset for fake news detection). These various aspects have proven (Shu et al. 2017 ) to have a great impact on the accuracy of fake news detection approaches.

Content-based issue, deceptive content

Automatic fake news detection remains a huge challenge, primarily because the content is designed in a way that it closely resembles the truth. Besides, most deceivers choose their words carefully and use their language strategically to avoid being caught. Therefore, it is often hard to determine its veracity by AI without the reliance on additional information from third parties such as fact-checkers.

Abdullah-All-Tanvir et al. ( 2020 ) reported that fake news tends to have more complicated stories and hardly ever make any references. It is more likely to contain a greater number of words that express negative emotions. This makes it so complicated that it becomes impossible for a human to manually detect the credibility of this content. Therefore, detecting fake news on social media is quite challenging. Moreover, fake news appears in multiple types and forms, which makes it hard and challenging to define a single global solution able to capture and deal with the disseminated content. Consequently, detecting false information is not a straightforward task due to its various types and forms Zannettou et al. ( 2019 ).

Contextual issues

Contextual issues are challenges that we suspect may not be related to the content of the news but rather they are inferred from the context of the online news post (i.e., humans are the weakest factor due to lack of user awareness, social bots spreaders, dynamic nature of online social platforms and fast propagation of fake news).

Humans are the weakest factor due to the lack of awareness

Recent statistics 31 show that the percentage of unintentional fake news spreaders (people who share fake news without the intention to mislead) over social media is five times higher than intentional spreaders. Moreover, another recent statistic 32 shows that the percentage of people who were confident about their ability to discern fact from fiction is ten times higher than those who were not confident about the truthfulness of what they are sharing. As a result, we can deduce the lack of human awareness about the ascent of fake news.

Public susceptibility and lack of user awareness (Sharma et al. 2019 ) have always been the most challenging problem when dealing with fake news and misinformation. This is a complex issue because many people believe almost everything on the Internet and the ones who are new to digital technology or have less expertise may be easily fooled (Edgerly et al. 2020 ).

Moreover, it has been widely proven (Metzger et al. 2020 ; Edgerly et al. 2020 ) that people are often motivated to support and accept information that goes with their preexisting viewpoints and beliefs, and reject information that does not fit in as well. Hence, Shu et al. ( 2017 ) illustrate an interesting correlation between fake news spread and psychological and cognitive theories. They further suggest that humans are more likely to believe information that confirms their existing views and ideological beliefs. Consequently, they deduce that humans are naturally not very good at differentiating real information from fake information.

Recent research by Giachanou et al. ( 2020 ) studies the role of personality and linguistic patterns in discriminating between fake news spreaders and fact-checkers. They classify a user as a potential fact-checker or a potential fake news spreader based on features that represent users’ personality traits and linguistic patterns used in their tweets. They show that leveraging personality traits and linguistic patterns can improve the performance in differentiating between checkers and spreaders.

Furthermore, several researchers studied the prevalence of fake news on social networks during (Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ) and after (Garrett and Bond 2021 ) the 2016 US presidential election and found that individuals most likely to engage with fake news sources were generally conservative-leaning, older, and highly engaged with political news.

Metzger et al. ( 2020 ) examine how individuals evaluate the credibility of biased news sources and stories. They investigate the role of both cognitive dissonance and credibility perceptions in selective exposure to attitude-consistent news information. They found that online news consumers tend to perceive attitude-consistent news stories as more accurate and more credible than attitude-inconsistent stories.

Similarly, Edgerly et al. ( 2020 ) explore the impact of news headlines on the audience’s intent to verify whether given news is true or false. They concluded that participants exhibit higher intent to verify the news only when they believe the headline to be true, which is predicted by perceived congruence with preexisting ideological tendencies.

Luo et al. ( 2022 ) evaluate the effects of endorsement cues in social media on message credibility and detection accuracy. Results showed that headlines associated with a high number of likes increased credibility, thereby enhancing detection accuracy for real news but undermining accuracy for fake news. Consequently, they highlight the urgency of empowering individuals to assess both news veracity and endorsement cues appropriately on social media.

Moreover, misinformed people are a greater problem than uninformed people (Kuklinski et al. 2000 ), because the former hold inaccurate opinions (which may concern politics, climate change, medicine) that are harder to correct. Indeed, people find it difficult to update their misinformation-based beliefs even after they have been proved to be false (Flynn et al. 2017 ). Moreover, even if a person has accepted the corrected information, his/her belief may still affect their opinion (Nyhan and Reifler 2015 ).

Falling for disinformation may also be explained by a lack of critical thinking and of the need for evidence that supports information (Vilmer et al. 2018 ; Badawy et al. 2019 ). However, it is also possible that people choose misinformation because they engage in directionally motivated reasoning (Badawy et al. 2019 ; Flynn et al. 2017 ). Online clients are normally vulnerable and will, in general, perceive web-based networking media as reliable, as reported by Abdullah-All-Tanvir et al. ( 2019 ), who propose to mechanize fake news recognition.

It is worth noting that in addition to bots causing the outpouring of the majority of the misrepresentations, specific individuals are also contributing a large share of this issue (Abdullah-All-Tanvir et al. 2019 ). Furthermore, Vosoughi et al. (Vosoughi et al. 2018 ) found that contrary to conventional wisdom, robots have accelerated the spread of real and fake news at the same rate, implying that fake news spreads more than the truth because humans, not robots, are more likely to spread it.

In this case, verified users and those with numerous followers were not necessarily responsible for spreading misinformation of the corrupted posts (Abdullah-All-Tanvir et al. 2019 ).

Viral fake news can cause much havoc to our society. Therefore, to mitigate the negative impact of fake news, it is important to analyze the factors that lead people to fall for misinformation and to further understand why people spread fake news (Cheng et al. 2020 ). Measuring the accuracy, credibility, veracity and validity of news contents can also be a key countermeasure to consider.

Social bots spreaders

Several authors (Shu et al. 2018b , 2017 ; Shi et al. 2019 ; Bessi and Ferrara 2016 ; Shao et al. 2018a ) have also shown that fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots (Ferrara et al. 2016 ). Bots (short for software robots) exist since the early days of computers. A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior (Ferrara et al. 2016 ). Although they are designed to provide a useful service, they can be harmful, for example when they contribute to the spread of unverified information or rumors (Ferrara et al. 2016 ). However, it is important to note that bots are simply tools created and maintained by humans for some specific hidden agendas.

Social bots tend to connect with legitimate users instead of other bots. They try to act like a human with fewer words and fewer followers on social media. This contributes to the forwarding of fake news (Jiang et al. 2019 ). Moreover, there is a difference between bot-generated and human-written clickbait (Le et al. 2019 ).

Many researchers have addressed ways of identifying and analyzing possible sources of fake news spread in social media. Recent research by Shu et al. ( 2020a ) describes social bots use of two strategies to spread low-credibility content. First, they amplify interactions with content as soon as it is created to make it look legitimate and to facilitate its spread across social networks. Next, they try to increase public exposure to the created content and thus boost its perceived credibility by targeting influential users that are more likely to believe disinformation in the hope of getting them to “repost” the fabricated content. They further discuss the social bot detection systems taxonomy proposed by Ferrara et al. ( 2016 ) which divides bot detection methods into three classes: (1) graph-based, (2) crowdsourcing and (3) feature-based social bot detection methods.

Similarly, Shao et al. ( 2018a ) examine social bots and how they promote the spread of misinformation through millions of Twitter posts during and following the 2016 US presidential campaign. They found that social bots played a disproportionate role in spreading articles from low-credibility sources by amplifying such content in the early spreading moments and targeting users with many followers through replies and mentions to expose them to this content and induce them to share it.

Ismailov et al. ( 2020 ) assert that the techniques used to detect bots depend on the social platform and the objective. They note that a malicious bot designed to make friends with as many accounts as possible will require a different detection approach than a bot designed to repeatedly post links to malicious websites. Therefore, they identify two models for detecting malicious accounts, each using a different set of features. Social context models achieve detection by examining features related to an account’s social presence including features such as relationships to other accounts, similarities to other users’ behaviors, and a variety of graph-based features. User behavior models primarily focus on features related to an individual user’s behavior, such as frequency of activities (e.g., number of tweets or posts per time interval), patterns of activity and clickstream sequences.

Therefore, it is crucial to consider bot detection techniques to distinguish bots from normal users to better leverage user profile features to detect fake news.

However, there is also another “bot-like” strategy that aims to massively promote disinformation and fake content in social platforms, which is called bot farms or also troll farms. It is not social bots, but it is a group of organized individuals engaging in trolling or bot-like promotion of narratives in a coordinated fashion (Wardle 2018 ) hired to massively spread fake news or any other harmful content. A prominent troll farm example is the Russia-based Internet Research Agency (IRA), which disseminated inflammatory content online to influence the outcome of the 2016 U.S. presidential election. 33 As a result, Twitter suspended accounts connected to the IRA and deleted 200,000 tweets from Russian trolls (Jamieson 2020 ). Another example to mention in this category is review bombing (Moro and Birt 2022 ). Review bombing refers to coordinated groups of people massively performing the same negative actions online (e.g., dislike, negative review/comment) on an online video, game, post, product, etc., in order to reduce its aggregate review score. The review bombers can be both humans and bots coordinated in order to cause harm and mislead people by falsifying facts.

Dynamic nature of online social platforms and fast propagation of fake news

Sharma et al. ( 2019 ) affirm that the fast proliferation of fake news through social networks makes it hard and challenging to assess the information’s credibility on social media. Similarly, Qian et al. ( 2018 ) assert that fake news and fabricated content propagate exponentially at the early stage of its creation and can cause a significant loss in a short amount of time (Friggeri et al. 2014 ) including manipulating the outcome of political events (Liu and Wu 2018 ; Bessi and Ferrara 2016 ).

Moreover, while analyzing the way source and promoters of fake news operate over the web through multiple online platforms, Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to real information (11%).

Furthermore, recently, Shu et al. ( 2020c ) attempted to understand the propagation of disinformation and fake news in social media and found that such content is produced and disseminated faster and easier through social media because of the low barriers that prevent doing so. Similarly, Shu et al. ( 2020b ) studied hierarchical propagation networks for fake news detection. They performed a comparative analysis between fake and real news from structural, temporal and linguistic perspectives. They demonstrated the potential of using these features to detect fake news and they showed their effectiveness for fake news detection as well.

Lastly, Abdullah-All-Tanvir et al. ( 2020 ) note that it is almost impossible to manually detect the sources and authenticity of fake news effectively and efficiently, due to its fast circulation in such a small amount of time. Therefore, it is crucial to note that the dynamic nature of the various online social platforms, which results in the continued rapid and exponential propagation of such fake content, remains a major challenge that requires further investigation while defining innovative solutions for fake news detection.

Datasets issue

The existing approaches lack an inclusive dataset with derived multidimensional information to detect fake news characteristics to achieve higher accuracy of machine learning classification model performance (Nyow and Chua 2019 ). These datasets are primarily dedicated to validating the machine learning model and are the ultimate frame of reference to train the model and analyze its performance. Therefore, if a researcher evaluates their model based on an unrepresentative dataset, the validity and the efficiency of the model become questionable when it comes to applying the fake news detection approach in a real-world scenario.

Moreover, several researchers (Shu et al. 2020d ; Wang et al. 2020 ; Pathak and Srihari 2019 ; Przybyla 2020 ) believe that fake news is diverse and dynamic in terms of content, topics, publishing methods and media platforms, and sophisticated linguistic styles geared to emulate true news. Consequently, training machine learning models on such sophisticated content requires large-scale annotated fake news data that are difficult to obtain (Shu et al. 2020d ).

Therefore, datasets are also a great topic to work on to enhance data quality and have better results while defining our solutions. Adversarial learning techniques (e.g., GAN, SeqGAN) can be used to provide machine-generated data that can be used to train deeper models and build robust systems to detect fake examples from the real ones. This approach can be used to counter the lack of datasets and the scarcity of data available to train models.

Fake news detection literature review

Fake news detection in social networks is still in the early stage of development and there are still challenging issues that need further investigation. This has become an emerging research area that is attracting huge attention.

There are various research studies on fake news detection in online social networks. Few of them have focused on the automatic detection of fake news using artificial intelligence techniques. In this section, we review the existing approaches used in automatic fake news detection, as well as the techniques that have been adopted. Then, a critical discussion built on a primary classification scheme based on a specific set of criteria is also emphasized.

Categories of fake news detection

In this section, we give an overview of most of the existing automatic fake news detection solutions adopted in the literature. A recent classification by Sharma et al. ( 2019 ) uses three categories of fake news identification methods. Each category is further divided based on the type of existing methods (i.e., content-based, feedback-based and intervention-based methods). However, a review of the literature for fake news detection in online social networks shows that the existing studies can be classified into broader categories based on two major aspects that most authors inspect and make use of to define an adequate solution. These aspects can be considered as major sources of extracted information used for fake news detection and can be summarized as follows: the content-based (i.e., related to the content of the news post) and the contextual aspect (i.e., related to the context of the news post).

Consequently, the studies we reviewed can be classified into three different categories based on the two aspects mentioned above (the third category is hybrid). As depicted in Fig.  5 , fake news detection solutions can be categorized as news content-based approaches, the social context-based approaches that can be divided into network and user-based approaches, and hybrid approaches. The latter combines both content-based and contextual approaches to define the solution.

Classification of fake news detection approaches

News Content-based Category

News content-based approaches are fake news detection approaches that use content information (i.e., information extracted from the content of the news post) and that focus on studying and exploiting the news content in their proposed solutions. Content refers to the body of the news, including source, headline, text and image-video, which can reflect subtle differences.

Researchers of this category rely on content-based detection cues (i.e., text and multimedia-based cues), which are features extracted from the content of the news post. Text-based cues are features extracted from the text of the news, whereas multimedia-based cues are features extracted from the images and videos attached to the news. Figure  6 summarizes the most widely used news content representation (i.e., text and multimedia/images) and detection techniques (i.e., machine learning (ML), deep Learning (DL), natural language processing (NLP), fact-checking, crowdsourcing (CDS) and blockchain (BKC)) in news content-based category of fake news detection approaches. Most of the reviewed research works based on news content for fake news detection rely on the text-based cues (Kapusta et al. 2019 ; Kaur et al. 2020 ; Vereshchaka et al. 2020 ; Ozbay and Alatas 2020 ; Wang 2017 ; Nyow and Chua 2019 ; Hosseinimotlagh and Papalexakis 2018 ; Abdullah-All-Tanvir et al. 2019 , 2020 ; Mahabub 2020 ; Bahad et al. 2019 ; Hiriyannaiah et al. 2020 ) extracted from the text of the news content including the body of the news and its headline. However, a few researchers such as Vishwakarma et al. ( 2019 ) and Amri et al. ( 2022 ) try to recognize text from the associated image.

News content-based category: news content representation and detection techniques

Most researchers of this category rely on artificial intelligence (AI) techniques (such as ML, DL and NLP models) to improve performance in terms of prediction accuracy. Others use different techniques such as fact-checking, crowdsourcing and blockchain. Specifically, the AI- and ML-based approaches in this category are trying to extract features from the news content, which they use later for content analysis and training tasks. In this particular case, the extracted features are the different types of information considered to be relevant for the analysis. Feature extraction is considered as one of the best techniques to reduce data size in automatic fake news detection. This technique aims to choose a subset of features from the original set to improve classification performance (Yazdi et al. 2020 ).

Table  6 lists the distinct features and metadata, as well as the used datasets in the news content-based category of fake news detection approaches.

The features and datasets used in the news content-based approaches

a https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

b https://mediabiasfactcheck.com/ , last access date: 26-12-2022

c https://github.com/KaiDMML/FakeNewsNet , last access date: 26-12-2022

d https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

e https://www.cs.ucsb.edu/~william/data/liar_dataset.zip , last access date: 26-12-2022

f https://www.kaggle.com/mrisdal/fake-news , last access date: 26-12-2022

g https://github.com/BuzzFeedNews/2016-10-facebook-fact-check , last access date: 26-12-2022

h https://www.politifact.com/subjects/fake-news/ , last access date: 26-12-2022

i https://www.kaggle.com/rchitic17/real-or-fake , last access date: 26-12-2022

j https://www.kaggle.com/jruvika/fake-news-detection , last access date: 26-12-2022

k https://github.com/MKLab-ITI/image-verification-corpus , last access date: 26-12-2022

l https://drive.google.com/file/d/14VQ7EWPiFeGzxp3XC2DeEHi-BEisDINn/view , last access date: 26-12-2022

Social Context-based Category

Unlike news content-based solutions, the social context-based approaches capture the skeptical social context of the online news (Zhang and Ghorbani 2020 ) rather than focusing on the news content. The social context-based category contains fake news detection approaches that use the contextual aspects (i.e., information related to the context of the news post). These aspects are based on social context and they offer additional information to help detect fake news. They are the surrounding data outside of the fake news article itself, where they can be an essential part of automatic fake news detection. Some useful examples of contextual information may include checking if the news itself and the source that published it are credible, checking the date of the news or the supporting resources, and checking if any other online news platforms are reporting the same or similar stories (Zhang and Ghorbani 2020 ).

Social context-based aspects can be classified into two subcategories, user-based and network-based, and they can be used for context analysis and training tasks in the case of AI- and ML-based approaches. User-based aspects refer to information captured from OSN users such as user profile information (Shu et al. 2019b ; Wang et al. 2019c ; Hamdi et al. 2020 ; Nyow and Chua 2019 ; Jiang et al. 2019 ) and user behavior (Cardaioli et al. 2020 ) such as user engagement (Uppada et al. 2022 ; Jiang et al. 2019 ; Shu et al. 2018b ; Nyow and Chua 2019 ) and response (Zhang et al. 2019a ; Qian et al. 2018 ). Meanwhile, network-based aspects refer to information captured from the properties of the social network where the fake content is shared and disseminated such as news propagation path (Liu and Wu 2018 ; Wu and Liu 2018 ) (e.g., propagation times and temporal characteristics of propagation), diffusion patterns (Shu et al. 2019a ) (e.g., number of retweets, shares), as well as user relationships (Mishra 2020 ; Hamdi et al. 2020 ; Jiang et al. 2019 ) (e.g., friendship status among users).

Figure  7 summarizes some of the most widely adopted social context representations, as well as the most used detection techniques (i.e., AI, ML, DL, fact-checking and blockchain), in the social context-based category of approaches.

Social context-based category: social context representation and detection techniques

Table  7 lists the distinct features and metadata, the adopted detection cues, as well as the used datasets, in the context-based category of fake news detection approaches.

The features, detection cues and datasets used int the social context-based approaches

a https://www.dropbox.com/s/7ewzdrbelpmrnxu/rumdetect2017.zip , last access date: 26-12-2022 b https://snap.stanford.edu/data/ego-Twitter.html , last access date: 26-12-2022

Hybrid approaches

Most researchers are focusing on employing a specific method rather than a combination of both content- and context-based methods. This is because some of them (Wu and Rao 2020 ) believe that there still some challenging limitations in the traditional fusion strategies due to existing feature correlations and semantic conflicts. For this reason, some researchers focus on extracting content-based information, while others are capturing some social context-based information for their proposed approaches.

However, it has proven challenging to successfully automate fake news detection based on just a single type of feature (Ruchansky et al. 2017 ). Therefore, recent directions tend to do a mixture by using both news content-based and social context-based approaches for fake news detection.

Table  8 lists the distinct features and metadata, as well as the used datasets, in the hybrid category of fake news detection approaches.

The features and datasets used in the hybrid approaches

Fake news detection techniques

Another vision for classifying automatic fake news detection is to look at techniques used in the literature. Hence, we classify the detection methods based on the techniques into three groups:

• Human-based techniques: This category mainly includes the use of crowdsourcing and fact-checking techniques, which rely on human knowledge to check and validate the veracity of news content.
• Artificial Intelligence-based techniques: This category includes the most used AI approaches for fake news detection in the literature. Specifically, these are the approaches in which researchers use classical ML, deep learning techniques such as convolutional neural network (CNN), recurrent neural network (RNN), as well as natural language processing (NLP).
• Blockchain-based techniques: This category includes solutions using blockchain technology to detect and mitigate fake news in social media by checking source reliability and establishing the traceability of the news content.

Human-based Techniques

One specific research direction for fake news detection consists of using human-based techniques such as crowdsourcing (Pennycook and Rand 2019 ; Micallef et al. 2020 ) and fact-checking (Vlachos and Riedel 2014 ; Chung and Kim 2021 ; Nyhan et al. 2020 ) techniques.

These approaches can be considered as low computational requirement techniques since both rely on human knowledge and expertise for fake news detection. However, fake news identification cannot be addressed solely through human force since it demands a lot of effort in terms of time and cost, and it is ineffective in terms of preventing the fast spread of fake content.

Crowdsourcing. Crowdsourcing approaches (Kim et al. 2018 ) are based on the “wisdom of the crowds” (Collins et al. 2020 ) for fake content detection. These approaches rely on the collective contributions and crowd signals (Tschiatschek et al. 2018 ) of a group of people for the aggregation of crowd intelligence to detect fake news (Tchakounté et al. 2020 ) and to reduce the spread of misinformation on social media (Pennycook and Rand 2019 ; Micallef et al. 2020 ).

Micallef et al. ( 2020 ) highlight the role of the crowd in countering misinformation. They suspect that concerned citizens (i.e., the crowd), who use platforms where disinformation appears, can play a crucial role in spreading fact-checking information and in combating the spread of misinformation.

Recently Tchakounté et al. ( 2020 ) proposed a voting system as a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party site.

Similarly, Huffaker et al. ( 2020 ) propose a crowdsourced detection of emotionally manipulative language. They introduce an approach that transforms classification problems into a comparison task to mitigate conflation content by allowing the crowd to detect text that uses manipulative emotional language to sway users toward positions or actions. The proposed system leverages anchor comparison to distinguish between intrinsically emotional content and emotionally manipulative language.

La Barbera et al. ( 2020 ) try to understand how people perceive the truthfulness of information presented to them. They collect data from US-based crowd workers, build a dataset of crowdsourced truthfulness judgments for political statements, and compare it with expert annotation data generated by fact-checkers such as PolitiFact.

Coscia and Rossi ( 2020 ) introduce a crowdsourced flagging system that consists of online news flagging. The bipolar model of news flagging attempts to capture the main ingredients that they observe in empirical research on fake news and disinformation.

Unlike the previously mentioned researchers who focus on news content in their approaches, Pennycook and Rand ( 2019 ) focus on using crowdsourced judgments of the quality of news sources to combat social media disinformation.

Fact-Checking. The fact-checking task is commonly manually performed by journalists to verify the truthfulness of a given claim. Indeed, fact-checking features are being adopted by multiple online social network platforms. For instance, Facebook 34 started addressing false information through independent fact-checkers in 2017, followed by Google 35 the same year. Two years later, Instagram 36 followed suit. However, the usefulness of fact-checking initiatives is questioned by journalists 37 , as well as by researchers such as Andersen and Søe ( 2020 ). On the other hand, work is being conducted to boost the effectiveness of these initiatives to reduce misinformation (Chung and Kim 2021 ; Clayton et al. 2020 ; Nyhan et al. 2020 ).

Most researchers use fact-checking websites (e.g., politifact.com, 38 snopes.com, 39 Reuters, 40 , etc.) as data sources to build their datasets and train their models. Therefore, in the following, we specifically review examples of solutions that use fact-checking (Vlachos and Riedel 2014 ) to help build datasets that can be further used in the automatic detection of fake content.

Yang et al. ( 2019a ) use PolitiFact fact-checking website as a data source to train, tune, and evaluate their model named XFake, on political data. The XFake system is an explainable fake news detector that assists end users to identify news credibility. The fakeness of news items is detected and interpreted considering both content and contextual (e.g., statements) information (e.g., speaker).

Based on the idea that fact-checkers cannot clean all data, and it must be a selection of what “matters the most” to clean while checking a claim, Sintos et al. ( 2019 ) propose a solution to help fact-checkers combat problems related to data quality (where inaccurate data lead to incorrect conclusions) and data phishing. The proposed solution is a combination of data cleaning and perturbation analysis to avoid uncertainties and errors in data and the possibility that data can be phished.

Tchechmedjiev et al. ( 2019 ) propose a system named “ClaimsKG” as a knowledge graph of fact-checked claims aiming to facilitate structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. “ClaimsKG” designs the relationship between vocabularies. To gather vocabularies, a semi-automated pipeline periodically gathers data from popular fact-checking websites regularly.

AI-based Techniques

Previous work by Yaqub et al. ( 2020 ) has shown that people lack trust in automated solutions for fake news detection However, work is already being undertaken to increase this trust, for instance by von der Weth et al. ( 2020 ).

Most researchers consider fake news detection as a classification problem and use artificial intelligence techniques, as shown in Fig.  8 . The adopted AI techniques may include machine learning ML (e.g., Naïve Bayes, logistic regression, support vector machine SVM), deep learning DL (e.g., convolutional neural networks CNN, recurrent neural networks RNN, long short-term memory LSTM) and natural language processing NLP (e.g., Count vectorizer, TF-IDF Vectorizer). Most of them combine many AI techniques in their solutions rather than relying on one specific approach.

Examples of the most widely used AI techniques for fake news detection

Many researchers are developing machine learning models in their solutions for fake news detection. Recently, deep neural network techniques are also being employed as they are generating promising results (Islam et al. 2020 ). A neural network is a massively parallel distributed processor with simple units that can store important information and make it available for use (Hiriyannaiah et al. 2020 ). Moreover, it has been proven (Cardoso Durier da Silva et al. 2019 ) that the most widely used method for automatic detection of fake news is not simply a classical machine learning technique, but rather a fusion of classical techniques coordinated by a neural network.

Some researchers define purely machine learning models (Del Vicario et al. 2019 ; Elhadad et al. 2019 ; Aswani et al. 2017 ; Hakak et al. 2021 ; Singh et al. 2021 ) in their fake news detection approaches. The more commonly used machine learning algorithms (Abdullah-All-Tanvir et al. 2019 ) for classification problems are Naïve Bayes, logistic regression and SVM.

Other researchers (Wang et al. 2019c ; Wang 2017 ; Liu and Wu 2018 ; Mishra 2020 ; Qian et al. 2018 ; Zhang et al. 2020 ; Goldani et al. 2021 ) prefer to do a mixture of different deep learning models, without combining them with classical machine learning techniques. Some even prove that deep learning techniques outperform traditional machine learning techniques (Mishra et al. 2022 ). Deep learning is one of the most widely popular research topics in machine learning. Unlike traditional machine learning approaches, which are based on manually crafted features, deep learning approaches can learn hidden representations from simpler inputs both in context and content variations (Bondielli and Marcelloni 2019 ). Moreover, traditional machine learning algorithms almost always require structured data and are designed to “learn” to act by understanding labeled data and then use it to produce new results with more datasets, which requires human intervention to “teach them” when the result is incorrect (Parrish 2018 ), while deep learning networks rely on layers of artificial neural networks (ANN) and do not require human intervention, as multilevel layers in neural networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes (Parrish 2018 ). The two most widely implemented paradigms in deep neural networks are recurrent neural networks (RNN) and convolutional neural networks (CNN).

Still other researchers (Abdullah-All-Tanvir et al. 2019 ; Kaliyar et al. 2020 ; Zhang et al. 2019a ; Deepak and Chitturi 2020 ; Shu et al. 2018a ; Wang et al. 2019c ) prefer to combine traditional machine learning and deep learning classification, models. Others combine machine learning and natural language processing techniques. A few combine deep learning models with natural language processing (Vereshchaka et al. 2020 ). Some other researchers (Kapusta et al. 2019 ; Ozbay and Alatas 2020 ; Ahmed et al. 2020 ) combine natural language processing with machine learning models. Furthermore, others (Abdullah-All-Tanvir et al. 2019 ; Kaur et al. 2020 ; Kaliyar 2018 ; Abdullah-All-Tanvir et al. 2020 ; Bahad et al. 2019 ) prefer to combine all the previously mentioned techniques (i.e., ML, DL and NLP) in their approaches.

Table  11 , which is relegated to the Appendix (after the bibliography) because of its size, shows a comparison of the fake news detection solutions that we have reviewed based on their main approaches, the methodology that was used and the models.

Comparison of AI-based fake news detection techniques

Blockchain-based Techniques for Source Reliability and Traceability

Another research direction for detecting and mitigating fake news in social media focuses on using blockchain solutions. Blockchain technology is recently attracting researchers’ attention due to the interesting features it offers. Immutability, decentralization, tamperproof, consensus, record keeping and non-repudiation of transactions are some of the key features that make blockchain technology exploitable, not just for cryptocurrencies, but also to prove the authenticity and integrity of digital assets.

However, the proposed blockchain approaches are few in number and they are fundamental and theoretical approaches. Specifically, the solutions that are currently available are still in research, prototype, and beta testing stages (DiCicco and Agarwal 2020 ; Tchechmedjiev et al. 2019 ). Furthermore, most researchers (Ochoa et al. 2019 ; Song et al. 2019 ; Shang et al. 2018 ; Qayyum et al. 2019 ; Jing and Murugesan 2018 ; Buccafurri et al. 2017 ; Chen et al. 2018 ) do not specify which fake news type they are mitigating in their studies. They mention news content in general, which is not adequate for innovative solutions. For that, serious implementations should be provided to prove the usefulness and feasibility of this newly developing research vision.

Table  9 shows a classification of the reviewed blockchain-based approaches. In the classification, we listed the following:

• The type of fake news that authors are trying to mitigate, which can be multimedia-based or text-based fake news.
• The techniques used for fake news mitigation, which can be either blockchain only, or blockchain combined with other techniques such as AI, Data mining, Truth-discovery, Preservation metadata, Semantic similarity, Crowdsourcing, Graph theory and SIR model (Susceptible, Infected, Recovered).
• The feature that is offered as an advantage of the given solution (e.g., Reliability, Authenticity and Traceability). Reliability is the credibility and truthfulness of the news content, which consists of proving the trustworthiness of the content. Traceability aims to trace and archive the contents. Authenticity consists of checking whether the content is real and authentic.

A checkmark ( ✓ ) in Table  9 denotes that the mentioned criterion is explicitly mentioned in the proposed solution, while the empty dash (–) cell for fake news type denotes that it depends on the case: The criterion was either not explicitly mentioned (e.g., fake news type) in the work or the classification does not apply (e.g., techniques/other).

A classification of popular blockchain-based approaches for fake news detection in social media

After reviewing the most relevant state of the art for automatic fake news detection, we classify them as shown in Table  10 based on the detection aspects (i.e., content-based, contextual, or hybrid aspects) and the techniques used (i.e., AI, crowdsourcing, fact-checking, blockchain or hybrid techniques). Hybrid techniques refer to solutions that simultaneously combine different techniques from previously mentioned categories (i.e., inter-hybrid methods), as well as techniques within the same class of methods (i.e., intra-hybrid methods), in order to define innovative solutions for fake news detection. A hybrid method should bring the best of both worlds. Then, we provide a discussion based on different axes.

Fake news detection approaches classification

News content-based methods

Most of the news content-based approaches consider fake news detection as a classification problem and they use AI techniques such as classical machine learning (e.g., regression, Bayesian) as well as deep learning (i.e., neural methods such as CNN and RNN). More specifically, classification of social media content is a fundamental task for social media mining, so that most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags (Wu and Liu 2018 ). The main challenge facing these approaches is how to extract features in a way to reduce the data used to train their models and what features are the most suitable for accurate results.

Researchers using such approaches are motivated by the fact that the news content is the main entity in the deception process, and it is a straightforward factor to analyze and use while looking for predictive clues of deception. However, detecting fake news only from the content of the news is not enough because the news is created in a strategic intentional way to mimic the truth (i.e., the content can be intentionally manipulated by the spreader to make it look like real news). Therefore, it is considered to be challenging, if not impossible, to identify useful features (Wu and Liu 2018 ) and consequently tell the nature of such news solely from the content.

Moreover, works that utilize only the news content for fake news detection ignore the rich information and latent user intelligence (Qian et al. 2018 ) stored in user responses toward previously disseminated articles. Therefore, the auxiliary information is deemed crucial for an effective fake news detection approach.

Social context-based methods

The context-based approaches explore the surrounding data outside of the news content, which can be an effective direction and has some advantages in areas where the content approaches based on text classification can run into issues. However, most existing studies implementing contextual methods mainly focus on additional information coming from users and network diffusion patterns. Moreover, from a technical perspective, they are limited to the use of sophisticated machine learning techniques for feature extraction, and they ignore the usefulness of results coming from techniques such as web search and crowdsourcing which may save much time and help in the early detection and identification of fake content.

Hybrid approaches can simultaneously model different aspects of fake news such as the content-based aspects, as well as the contextual aspect based on both the OSN user and the OSN network patterns. However, these approaches are deemed more complex in terms of models (Bondielli and Marcelloni 2019 ), data availability, and the number of features. Furthermore, it remains difficult to decide which information among each category (i.e., content-based and context-based information) is most suitable and appropriate to be used to achieve accurate and precise results. Therefore, there are still very few studies belonging to this category of hybrid approaches.

Early detection

As fake news usually evolves and spreads very fast on social media, it is critical and urgent to consider early detection directions. Yet, this is a challenging task to do especially in highly dynamic platforms such as social networks. Both news content- and social context-based approaches suffer from this challenging early detection of fake news.

Although approaches that detect fake news based on content analysis face this issue less, they are still limited by the lack of information required for verification when the news is in its early stage of spread. However, approaches that detect fake news based on contextual analysis are most likely to suffer from the lack of early detection since most of them rely on information that is mostly available after the spread of fake content such as social engagement, user response, and propagation patterns. Therefore, it is crucial to consider both trusted human verification and historical data as an attempt to detect fake content during its early stage of propagation.

Conclusion and future directions

In this paper, we introduced the general context of the fake news problem as one of the major issues of the online deception problem in online social networks. Based on reviewing the most relevant state of the art, we summarized and classified existing definitions of fake news, as well as its related terms. We also listed various typologies and existing categorizations of fake news such as intent-based fake news including clickbait, hoax, rumor, satire, propaganda, conspiracy theories, framing as well as content-based fake news including text and multimedia-based fake news, and in the latter, we can tackle deepfake videos and GAN-generated fake images. We discussed the major challenges related to fake news detection and mitigation in social media including the deceptiveness nature of the fabricated content, the lack of human awareness in the field of fake news, the non-human spreaders issue (e.g., social bots), the dynamicity of such online platforms, which results in a fast propagation of fake content and the quality of existing datasets, which still limits the efficiency of the proposed solutions. We reviewed existing researchers’ visions regarding the automatic detection of fake news based on the adopted approaches (i.e., news content-based approaches, social context-based approaches, or hybrid approaches) and the techniques that are used (i.e., artificial intelligence-based methods; crowdsourcing, fact-checking, and blockchain-based methods; and hybrid methods), then we showed a comparative study between the reviewed works. We also provided a critical discussion of the reviewed approaches based on different axes such as the adopted aspect for fake news detection (i.e., content-based, contextual, and hybrid aspects) and the early detection perspective.

To conclude, we present the main issues for combating the fake news problem that needs to be further investigated while proposing new detection approaches. We believe that to define an efficient fake news detection approach, we need to consider the following:

• Our choice of sources of information and search criteria may have introduced biases in our research. If so, it would be desirable to identify those biases and mitigate them.
• News content is the fundamental source to find clues to distinguish fake from real content. However, contextual information derived from social media users and from the network can provide useful auxiliary information to increase detection accuracy. Specifically, capturing users’ characteristics and users’ behavior toward shared content can be a key task for fake news detection.
• Moreover, capturing users’ historical behavior, including their emotions and/or opinions toward news content, can help in the early detection and mitigation of fake news.
• Furthermore, adversarial learning techniques (e.g., GAN, SeqGAN) can be considered as a promising direction for mitigating the lack and scarcity of available datasets by providing machine-generated data that can be used to train and build robust systems to detect the fake examples from the real ones.
• Lastly, analyzing how sources and promoters of fake news operate over the web through multiple online platforms is crucial; Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to valid information (11%).

Appendix: A Comparison of AI-based fake news detection techniques

This Appendix consists only in the rather long Table  11 . It shows a comparison of the fake news detection solutions based on artificial intelligence that we have reviewed according to their main approaches, the methodology that was used, and the models, as explained in Sect.  6.2.2 .

Author Contributions

The order of authors is alphabetic as is customary in the third author’s field. The lead author was Sabrine Amri, who collected and analyzed the data and wrote a first draft of the paper, all along under the supervision and tight guidance of Esma Aïmeur. Gilles Brassard reviewed, criticized and polished the work into its final form.

This work is supported in part by Canada’s Natural Sciences and Engineering Research Council.

Availability of data and material

Declarations.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Contributor Information

Esma Aïmeur, Email: ac.laertnomu.ori@ruemia .

Sabrine Amri, Email: [email protected] .

Gilles Brassard, Email: ac.laertnomu.ori@drassarb .

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7 Negative Effects of Social Media on People and Users

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If you can't imagine your life without social media, you've probably fallen victim to the strong power that social networking sites have over the public. Chances are that you've also experienced some of the negative effects social media has on people.

Unfortunately, the bad effects of social media are all too real for a lot of us. Let's look at the negative impacts of social media on real people, so you can recognize these symptoms and get help if needed.

How Social Media Is Bad for You

You might be surprised to learn that the negative effects of social media are both physical and mental. They can change your perception of the world and yourself. While social media does have some positive effects , and there are certainly positive social media stories , it also has a lot in the drawback column.

Don't believe this? Read on for a list of social media's negative effects. If you recognize any of them as issues in your own life, it may be time to reduce your usage or even stop using social media altogether.

1. Depression and Anxiety

Do you spend several hours per day browsing through social media? Spending too long on social networking sites could adversely affect your mood. In fact, chronic social users are more likely to report poor mental health, including symptoms of anxiety and depression.

It doesn't take much thinking to figure out why. Social media lets you see the carefully selected best parts of everyone else's lives, which you then compare to the negatives in your own life (that only you see). Comparing yourself to other people is a sure path to anxiety and unhappiness, and social media has made this much easier to do.

So how do you use social media without causing yourself psychological distress? If you turn to the same research (and common sense), the recommended amount of time you should spend on social networks is around half an hour per day. As with many other potential ills in life, it's all about moderation.

If you find yourself upset after a social media session , also consider the networks you use and the people you follow. You're much more likely to feel anxious after reading political arguments and doomsday news than you are after seeing fun updates from your favorite musicians or photos of your friends' pets.

2. Cyberbullying

Before social media, bullying was something that was only possible to do face-to-face. However, now people can bully others online—anonymously or not. Today everyone knows what cyberbullying is , and most of us have seen what it can do to a person.

While social media makes it easier to meet new people and make friends, it also enables cruel people to tear into others with little effort. Perpetrators of bullying can use the anonymity that (some) social networks provide to gain people's trust and then terrorize them in front of their peers. For instance, they might create a fake profile and act friendly to a classmate, then later betray and embarrass them online.

These online attacks often leave deep mental scars and even drive people to hurt themselves or take their own lives, in some cases. And as it turns out, cyberbullying doesn't just affect kids. Adults can become victims of online abuse, too. Since screens hide our faces, you can end up being a jerk on social media and other websites without even realizing it.

Learn how to make your Instagram profile more private , and apply the same advice to other social networks, if you suffer from this issue.

3. FOMO (Fear of Missing Out)

Fear of Missing Out (FOMO) is a phenomenon that became prominent around the same time as the rise of social media. Unsurprisingly, it's one of the most widespread negative effects of social media on society.

FOMO is just what it sounds like: a form of anxiety that you get when you're scared of missing out on a positive experience that someone else is having. For example, you might constantly check your messages to see if anyone has invited you out, or focus on your Instagram feed all day to make sure that nobody is doing something cool without you. You may also see pictures of something fun that your friends were able to do, feeling left out that you couldn't go because you had another responsibility.

This fear receives constant fuel from what you see on social media. With increased social network use, there's a better chance for you to see that someone is having more fun than you are right now. That's exactly what causes FOMO, so if you're prone to this, know how to prevent FOMO when using social media (or cut back on using it altogether).

4. Unrealistic Expectations

As most people are probably aware, social media forms unrealistic expectations of life and friendships in our minds.

Most social media sites have a severe lack of online authenticity. People use Snapchat to share their exciting adventures, post about how much they love their significant other on Facebook, and load up their Instagram page with heavily staged photos.

But in reality, you have no way of knowing whether this is all a farce. While it looks great on the surface, that person could be in massive debt, on bad terms with their significant other, or desperate for Instagram likes as a form of validation.

One simple way out of this mess is for everyone to quit lying on social media. But in the era of Instagram influencers and YouTubers who earn millions from being inauthentic, that isn't going to happen anytime soon.

Remember an important adage: you should not judge your everyday life against the highlights of someone else's.

5. Negative Body Image

Speaking of Instagram celebrities, if you look at popular Instagram accounts, you'll find unbelievably beautiful people wearing expensive clothes on their perfectly shaped bodies.

And to nobody's surprise, body image is now an issue for almost everyone. Of course, seeing so many people who are supposedly perfect (according to society's standards) on a daily basis makes you conscious of how different you look from those pictures. And not everyone comes to healthy conclusions in this situation.

It's really important to remember that everybody is human. No one wakes up every day looking like a supermodel, and while many people have gone to great lengths to train their bodies, that's not the case for everyone who looks fit. Many people, in search of social media fame, have definitely taken unhealthy routes to appear more attractive.

Surround yourself with people who love you for who you are, and you won't have to stress about fake Instagram beauty.

6. Unhealthy Sleep Patterns

On top of increasing the cases of anxiety and depression, another bad thing about social media is that spending too much time on it can lead to poor sleep. Numerous studies have shown that increased use of social media has a negative effect on your sleep quality.

If you feel that your sleep patterns have become irregular, leading to a drop in productivity, try to cut down on the amount of time you browse social media.

This is especially the case when using your phone in bed at night. It's all too easy to tell yourself that you'll spend five minutes checking your Facebook notifications, only to realize an hour later that you've been mindlessly scrolling through some nonsense on Twitter you don't even care about.

Don't let social media algorithms, which are designed to keep your attention for as long as possible, steal your valuable sleep too. Getting less sleep, combined with that sleep being lower quality, is a dangerous, unhealthy combination.

7. General Addiction

Social media can be more addictive than cigarettes and alcohol. It has a powerful draw for many people that leads to them checking it all the time without even thinking about it.

If you're not sure whether you're addicted to social networks, try to remember the last time you went a full day without checking any social media accounts. Do you feel rejected if someone unfollows you? And if your favorite social networks completely disappeared tomorrow, would the absence make you feel empty and depressed?

At the end of the day, social media sites want to keep you scrolling for as long as possible so they can show you lots of ads and make more money. Because of the attention economy , these sites need your eyes on them for as long as possible. Apps like TikTok feed you a constant barrage of quick videos that destroy your attention span over time.

Just because you've been going overboard on social media use doesn't mean you necessarily need to wipe out all your social networking accounts. However, if you think quitting is the best solution for you, it isn't a bad idea. See our guide to quitting social media for good if you'd like help.

How to Handle the Negative Effects of Social Media

As with everything else, there are good and bad aspects of social media. We've discussed some of the negative impacts social media has for many, but you're the one who must decide whether there's more help or harm in it for you personally.

If you find that social media is having a negative impact on your life, stop using it. However, if you decide to stay, there are ways to waste less time on social media, and thus maintain a healthier relationship with it.

• Social Media
• Mental Health