annotated explanation text

How to Write an Excellent Explanation Text

Welcome to our complete guide to writing an explanation text.  This guide is intended for both teachers and students to make the process of writing fun and straightforward.

This page has plenty of great content, and downloadable resources such as graphic organizer prompts and much more.  If you like what you see here, check out all our other writing genre guides.

WHAT IS AN EXPLANATION TEXT?

An explanation text tells your audience how something works or why something happens.

Explanations detail and logically describe the stages in a process , such as the water cycle or how a steam engine works.  Other examples could be how a law is made or why we blink when we sneeze.

Explanation texts are frequently incorporated into other texts, used to provide information which answers questions of interest on that topic.

TYPES OF EXPLANATION TEXT

Not all explanation texts are created equal, and they vary in complexity to research and construct. As such, we have listed them from easiest to most challenging.

• Sequential Explanations – These detail the stages in an event, e.g., how a caterpillar turns into a moth. These are excellent starting points for younger writers and those new to this genre of writing.
• Causal Explanations – Details what causes the change from one stage to the next, i.e., How a president is elected.
• Theoretical Explanations – Details the possible phenomena behind a natural or created process that is not fully understood. e.g. What caused the Nazis to lose World War II?
• Scientific– e.g. Explain the causes of climate change (Factorial)
• Historical– e.g. Explain the causes of World War 2 (Factorial)

DON’T GET CONFUSED BETWEEN EXPLANATION TEXTS AND PROCEDURAL TEXTS

An explanatory text has some similarities to a procedural text , and these can often be confused; however, an explanation text explains the how and why behind a process, such as  

• What causes a Tsunami?
• Why are our rainforests disappearing?
• The process of making aluminium.

A procedural text is about writing logical and efficient instructions to complete a task. It is all about the ‘how,’ whereas an explanation focuses more on the ‘why.’ So as we can see, although they are similar, both text types are very different in purpose.

A COMPLETE UNIT ON TEACHING EXPLANATION TEXTS

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STRUCTURE AND FEATURES OF AN EXPLANATORY ESSAY

Explanatory writing structure.

TITLES Which identify the topic of your explanation. You may pose this as a question at the beginning with how? or why?

STRONG OPENING STATEMENT Identifying the process to be explained. Emphasize the process rather than the particular thing involved in the process.

SEQUENCING Use sequential paragraphs or statements describing how or why something happens. Show connections such as cause and effect or temporal sequence.

WRAPPING IT UP A strong concluding paragraph or sentence that draws everything together will add more validity to your explanation.

EXPLANATORY WRITING FEATURES

GENERALIZE Talk about your topics in groups or as a collective rather than as individuals.

CONNECTIVE LANGUAGE Use language which link cause and effect.

GET TECHNICAL & DETAILED Use technical language and terms specific to your subject. Use technical descriptions to crate richer meaning.

TENSE AND VOICE Explanations are written in the passive voice and in timeless present tense

VISUALS Use graphic organizers, labelled diagrams and even videos you are constructing a digital text to illustrate your understanding.

THE LANGUAGE FEATURES OF AN EXPLANATION TEXT

• Use technical terms such as evaporation and degradation if writing a water cycle explanation text.
• Action verbs and present tense such as runs, develop and becomes
• Cause and effect terms such as because of.., due to.., therefore, and as a result

USE YOUR TIME EFFECTIVELY

Using your writing time effectively is really crucial.  When writing an explanation, you should aim to spend about one-fifth of your time researching your topic to ensure you know what you are talking about.

Next, take an equal amount of time to structure your writing using a graphic organizer or mind map, which can be found below. If you follow this model, you only need to spend under half your time writing.  Your ideas and structure will already be formed.

This will leave you a reasonable window to edit and revise your essay for meaning, spelling and grammar and structure. Using graphic organizers , planning tools, and writing checklists will greatly assist the planning and editing time.

HOW TO WRITE AN EXPLANATORY TEXT

Points to consider before writing your explanatory essay.

• What is it about? What are you explaining? Are you explaining how or why something happens or are you explaining a process?
• What is the title?
• What are the essential parts and sections of what you want to explain? How would you describe it and its parts? Which parts need to be described as part of the explanation?
• How does it work? What happens first, next, and why?
• What else might you include?

Introduction: Because you are explaining a process, your audience will require some context about your topic. Firstly, ensure you provide some facts and insights so that it makes sense to your audience.

Secondly, You have obviously found this subject interesting enough to write an essay about it, so ensure what piqued your interest is translated to your audience by creating a hook that leaves your audience wanting to read on.

Body: Keep everything in chronological order here to ensure your explanation follows a sequence.

In this section, you want your paragraphs to really emphasise what happens in the opening of each paragraph and then lead into how and, or why things occur using relevant technical terms and action verbs.

Use the bulk of your paragraph to focus on the how and why that will both fill your reader with wonder, and lead them to ask questions about your subject area.

Use connective terms and transitional language that is not repetitive when linking paragraphs. Be sure to read our complete guide to writing perfect paragraphs for further details.

Make sure that you have entirely covered the explanation before moving on to your conclusion.

Conclusion: Use the conclusion to pose and or answer any apparent questions the audience may have on your topic.

Also, feel free to share a very personal opinion or insight about your topic to build a connection with the audience to ensure your explanation text is worthy of their time.

TIPS FOR WRITING A GREAT EXPLANATION TEXT

Explanatory writing graphic organizer template.

EXPLANATORY TEXT WRITING PROMPTS

Explanatory text tutorial videos.

Teaching Resources

Use our resources and tools to improve your student’s writing skills through proven teaching strategies.

A COMPLETE UNIT OF WORK ON EXPLANATION WRITING?

We pride ourselves on being the web’s best resource for teaching students and teachers how to write an explanation text. We value the fact you have taken the time to read our comprehensive guides to understand the fundamentals of writing stories.

We also understand some of you just don’t have the luxury of time or resources to create engaging resources when needed.

If you are time-poor and looking for an in-depth solution that encompasses all of the concepts outlined in this article, I strongly recommend looking at the Excellent Explanation Text Writing Unit.

Working in partnership with Innovative Teaching Ideas , we confidently recommend this resource as an all-in-one solution to teaching explanatory texts.

This unit will find over 91 pages of engaging and innovative teaching ideas.

EXPLANATION TEXT WRITING CHECKLISTS FOR JUNIOR, MIDDLE & SENIOR STUDENTS

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OTHER GREAT ARTICLES RELATED TO EXPLANATION TEXT WRITING

Explanation Texts: Top 5 Writing tips for younger students

How to write a perfect 5 Paragraph Essay

5 Essential Informational Text Writing Skills to Master

5 Procedural Writing Lesson Plans Students and Teachers will Love

Annotating Texts

What is annotation.

Annotation can be:

• A systematic summary of the text that you create within the document
• A key tool for close reading that helps you uncover patterns, notice important words, and identify main points
• An active learning strategy that improves comprehension and retention of information

Why annotate?

• Isolate and organize important material
• Identify key concepts
• Monitor your learning as you read
• Make exam prep effective and streamlined
• Can be more efficient than creating a separate set of reading notes

How do you annotate?

Summarize key points in your own words .

• Use headers and words in bold to guide you
• Look for main ideas, arguments, and points of evidence
• Notice how the text organizes itself. Chronological order? Idea trees? Etc.

Circle key concepts and phrases

• What words would it be helpful to look-up at the end?
• What terms show up in lecture? When are different words used for similar concepts? Why?

Write brief comments and questions in the margins

• Be as specific or broad as you would like—use these questions to activate your thinking about the content
• See our handout on reading comprehension tips for some examples

Use abbreviations and symbols

• Try ? when you have a question or something you need to explore further
• Try ! When something is interesting, a connection, or otherwise worthy of note
• Try * For anything that you might use as an example or evidence when you use this information.
• Ask yourself what other system of symbols would make sense to you.

Highlight/underline

• Highlight or underline, but mindfully. Check out our resource on strategic highlighting for tips on when and how to highlight.

Use comment and highlight features built into pdfs, online/digital textbooks, or other apps and browser add-ons

• Are you using a pdf? Explore its highlight, edit, and comment functions to support your annotations
• Some browsers have add-ons or extensions that allow you to annotate web pages or web-based documents
• Does your digital or online textbook come with an annotation feature?
• Can your digital text be imported into a note-taking tool like OneNote, EverNote, or Google Keep? If so, you might be able to annotate texts in those apps

What are the most important takeaways?

• Annotation is about increasing your engagement with a text
• Increased engagement, where you think about and process the material then expand on your learning, is how you achieve mastery in a subject
• As you annotate a text, ask yourself: how would I explain this to a friend?
• Put things in your own words and draw connections to what you know and wonder

The table below demonstrates this process using a geography textbook excerpt (Press 2004):

A common concern about annotating texts: It takes time!

Yes, it can, but that time isn’t lost—it’s invested.

Spending the time to annotate on the front end does two important things:

• It saves you time later when you’re studying. Your annotated notes will help speed up exam prep, because you can review critical concepts quickly and efficiently.
• It increases the likelihood that you will retain the information after the course is completed. This is especially important when you are supplying the building blocks of your mind and future career.

One last tip: Try separating the reading and annotating processes! Quickly read through a section of the text first, then go back and annotate.

Works consulted:

Nist, S., & Holschuh, J. (2000). Active learning: strategies for college success. Boston: Allyn and Bacon. 202-218.

Simpson, M., & Nist, S. (1990). Textbook annotation: An effective and efficient study strategy for college students. Journal of Reading, 34: 122-129.

Press, F. (2004). Understanding earth (4th ed). New York: W.H. Freeman. 208-210.

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Space to find reference of english, explanation text; definition, generic structures, purposes, language features.

December 12, 2017 British Course Explanation of Text Genre 9

Assalamualaikum Warahmatullahi Wabarakatuh😊

Bismillahirrahmanirrahim

Hallo everybody BRITISH Course – I’m sure my friends have been familiar with the word “explanation”. Because in the world of education and even in daily life we usually meet explanation, maybe from our teachers, our parents, even our friends, right? For example, do you ever explain something to your friends? When your friend asked, how is the process of rain, or how is the process of human creation? etc. Then you explained to your friends about it. Those explanations are examples of Explanation Text.

Well, for my friends who want to learn about explanation text, my friends have come in the right place because in this occasion I will try to present explanations and examples of Explanation Text in detail and complete. I hope my explanation about Explanation Text below can be useful for all of the readers. Amen

Definition of Explanation Text

Explanation is a text which tells processes relating to forming of natural, social, scientific and cultural phenomena. Explanation text is to say ‘why’ and ‘how’ of the forming of the phenomena. It is often found in science, geography and history text books.

Generic Structure of Explanation Text

– General statement General statement; stating the phenomenon issues which are to be explained. – Sequenced of explanation Sequenced explanation; stating a series of steps which explain the phenomena.

Purpose of Explanation Text

– Explanation is a text which tells processes relating to forming of natural, social, scientific, and cultural phenomena. – To explain how or why something happens.

According to Mark Anderson and Kathy Anderson (1997: 82) says that the explanation text type is often used to tell how and why thing (phenomena) occur in nature.

Language Features of Explanation Text

In an explanation text, there are linguistic features as below : – Using simple present tense – Using abstract noun (no visible noun) – Using Passive voice – Using Action verbs – Containing explanation of the process ..

Examples of Explanation Text

Example of explanation text about natural phenomenon, how does rain happen.

Rain is the primary source of fresh water for most areas of the world, providing suitable conditions for diverse ecosystems, as well as water for hydroelectric power plants and crop irrigation.

The phenomenon of rain is actually a water circle. The concept of the water cycle involves the sun heating the Earth’s surface water and causing the surface water to evaporate. The water vapor rises into the Earth’s atmosphere. The water in the atmosphere cools and condenses into liquid droplets. The droplets grow until they are heavy and fall to the earth as precipitation which can be in the form of rain or snow.

However, not all rain reaches the surface. Some evaporates while falling through dry air. This is called virga, a phenomenon which is often seen in hot, dry desert regions.

Example of Explanation Text about Process of Making Chocolate

How chocolate is made.

Have we wondered how we get chocolate from? Well this time we will enter the amazing world of chocolate so we can understand exactly we are eating.

Chocolate starts a tree called cacao tree. This tree grows in equatorial regions, especially in place such as South America, Africa, and Indonesia. The cacao tree produces a fruit about the size of a small pine apple. In side the fruits are the tree’s seeds. They are also known as coco beans.

Next, the beans are fermented for about a week, dried in the sun. After that they are shipped to the chocolate maker. The chocolate maker starts processing by roasting the beans to bring out the flavour. Different beans from different places have different qualities and flavour. So they are often shorted and blended to produce a distinctive mix.

The next process is winnowing. The roasted beans are winnowed to remove the meat nib of the cacao bean from its shell. Then the nibs are blended. The blended nibs are grounded to make it liquid. The liquid is called chocolate liquor. It tastes bitter.

All seeds contain some amount of fat and cacao beans are not different. However, cacao beans are half fat, which is why they ground nibs from liquid. It is pure bitter chocolate.

Example of Explanation Text – How a Cancer is Formed

How a cancer is formed.

What is cancer? It is actually a group of more than one hundred separate diseases. Most of us are fear from cancer. It is reasonable because next to heart disease, cancer is the second leading cause of death.

Cancer cells come from normal cells because of mutations of DNA. Those mutations can occur spontaneously. The mutations may be also induced by other factors such as: nuclear and electromagnetic radiation, viruses, bacteria and fungi, parasites, heat, chemicals in the air, water and food, mechanical cell-level injury, free radicals, evolution and ageing of DNA, etc. All such factors can produce mutations that may start cancer.

Cancer cells are formed continuously in the organism. It is estimated that there are about 10,000 cancer cells at any given time in a healthy person. Why do some result in macroscopic-level cancers and some do not? First, not all damaged cells can multiply and many of them die quickly. Second, those which potentially divide and form cancer are effectively destroyed by the mechanisms available to the immune system. Therefore cancer develops if the immune system is not working properly or the amount of cells produced is too great for the immune system to eliminate.

Bagaimana Coklat Terbentuk

Sudahkah kita bertanya-tanya bagaimana kita mendapatkan cokelat? Nah kali ini kita akan memasuki dunia coklat yang menakjubkan sehingga kita bisa mengerti persis yang kita makan.

Coklat memulai pohon yang disebut pohon kakao. Pohon ini tumbuh di daerah khatulistiwa, terutama di tempat seperti Amerika Selatan, Afrika, dan Indonesia. Pohon kakao menghasilkan buah seukuran apel pinus kecil. Di sisi buah adalah biji pohon. Mereka juga dikenal sebagai coco beans.

Selanjutnya, biji difermentasi selama sekitar satu minggu, dikeringkan di bawah sinar matahari. Setelah itu mereka dikirim ke pembuat cokelat. Pembuat cokelat mulai memproses dengan memanggang biji coklat untuk mengeluarkan rasa. Biji yang berbeda dari tempat yang berbeda memiliki kualitas dan rasa yang berbeda. Jadi mereka sering disortir dan dicampur untuk menghasilkan campuran yang khas.

Proses selanjutnya adalah menampi. Biji coklat yang dipanggang diminyaki untuk mengeluarkan nib daging dari biji kakao dari cangkangnya. Kemudian nibs dicampur. Nibs yang dicampur digiling agar cair. Cairan itu disebut minuman cairan coklat. Rasanya pahit.

Semua biji mengandung sejumlah besar biji kakao dan lemak tidak berbeda. Namun, biji kakao setengah lemak, itulah sebabnya mereka menggiling nib dari cairan. Cokelat itu murni pahit.

Note on the Generic Structure of Explanation Sample

Every genre has its special purpose or social function. However if we see the generic structure point, we will get the understanding which both the explanation and procedure text have similar purposes. Both explain how to make or form something. However the procedure text will explain how to form or make something completely by instruction way. That is why most of procedure text is composed in command sentences. In the other hand, explanation text will show a knowledge about how thing is formed.

The above example of explanation text has the following generic structure :

General statement; it is a statement which says about chocolate and how it is formed

Sequenced explanation; it is a series of explanation on how chocolate is formed before we eat. First, the chocolate is coming from the cacao tree. Then it is fermented and ship to the chocolate producer. The cacao bean then are roasted and winnowed.

Example of Explanation Text – How A Fuel Light Works

How a fuel light works.

Many cars, motorcycles and other modern vehicles have fuel warning light devices. the warning light is usually red which switches on automatically when the level of fuel in the tank is very low. The warning light gives the driver information about the amount of petrol in the tank. When the light switches on red, it tells the driver that the petrol in the tank is almost empty. Therefore we have to put more fuel into the tank. However do you know how the fuel warning works?

Well this is the way the fuel warning light work and gives the driver information about the accurate amount of the petrol in the tank. When the level of the fuel falls, the float inside the tank moves downwards. When this condition happens, the arm also moves downwards and it make the lever touch an electrical contact. This switches on the fuel light in the car dashboard.

The red light which appears in the fuel panel of the dashboard tells the driver that he needs more petrol for his car. When he pours more petrol into the tank, this condition makes the fuel level rise and it pushes the float upwards. In return it disconnects to the electrical contact and makes the red light switch off.

Cara Kerja Cahaya Bahan Bakar

Banyak mobil, sepeda motor dan kendaraan modern lainnya memiliki lampu peringatan bahan bakar. Lampu peringatan biasanya merah yang menyala secara otomatis bila tingkat bahan bakar di dalam tangki sangat rendah. Lampu peringatan memberi informasi tentang jumlah bensin di tangki. Bila lampu menyala merah, berarti pengemudi nya bensin di tangki hampir kosong. Karena itu kita harus memasukkan lebih banyak bahan bakar ke dalam tangki. Namun tahukah anda bagaimana bahan bakar bekerja?

Nah begitulah cara kerja lampu peringatan bahan bakar dan memberi informasi pengemudi tentang jumlah bensin yang akurat di tangki. Bila tingkat bahan bakar turun, pelampung di dalam tangki bergerak ke bawah. Bila kondisi ini terjadi, lengan juga bergerak ke bawah dan itu membuat tuas menyentuh kontak listrik. Ini akan menyalakan lampu bahan bakar di dasbor mobil.

Lampu merah yang muncul di panel bahan bakar di dasbor memberi tahu pengemudi bahwa ia membutuhkan lebih banyak bensin untuk mobilnya. Saat menuangkan bensin lagi ke tangki, kondisi ini membuat tingkat bahan bakar naik dan mendorong float ke atas. Sebagai gantinya diputus ke kontak listrik dan mematikan lampu merah.

(Grammar – xx)

Related Articles : Report Text ; Definition, Generic Structures, Purposes, Language Features

Oke. I think that’s all my explanation about Explanation Text. I hope it will be useful for us. I hope you are never bored to visit this site. Don’t forget to visit this site if you need English reference in your study. See you next time..

Reference : Rudi Hartono, Genre of Texts, (Semarang: English Department Faculty of Language and Art Semarang State University, 2005). Text Genre, Grammar: Technologies for Teaching and Assessing Writing, Peter Knapp & Megan Watkins, New South Wales Press, Ltd : Australia Mark Andersons and Kathy Andersons, Text Type in English 1-2, Australia: MacMillanEducation, 2003.

Related posts:

• Explanation Text
• Descriptive Text (Penjelasan Dan Contoh)
• Narrative Text (Complete Explanation)
• Hortatory Exposition Text; Definition, Generic Structures, Purposes, Language Features

Why is the Hello written as ‘Hallo’ (lines 1) ?

I won’t to change my phone number because my phone is lost yesterday 240am

1)the purpose of the explanation text is to explain the process involved in the formation of a natural

2)the generic structure of the explanation text : a.general statement (this part is general introduction of the phenomeneon that we are going to explain) b.sequenced explanation (explains about some process involved of the phenomeneon) c.closing (presented in this part)

3)language features of explanation text: a.using simple present tense b.using passive voice c.focus on natural

Saya ingin bergabung

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I want to learn English better

Ok. Good Widaya 🙂

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Reading and Study Strategies

What is annotating and why do it, annotation explained, steps to annotating a source, annotating strategies.

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What is Annotating?

Annotating is any action that deliberately interacts with a text to enhance the reader's understanding of, recall of, and reaction to the text. Sometimes called "close reading," annotating usually involves highlighting or underlining key pieces of text and making notes in the margins of the text. This page will introduce you to several effective strategies for annotating a text that will help you get the most out of your reading.

Why Annotate?

By annotating a text, you will ensure that you understand what is happening in a text after you've read it. As you annotate, you should note the author's main points, shifts in the message or perspective of the text, key areas of focus, and your own thoughts as you read. However, annotating isn't just for people who feel challenged when reading academic texts. Even if you regularly understand and remember what you read, annotating will help you summarize a text, highlight important pieces of information, and ultimately prepare yourself for discussion and writing prompts that your instructor may give you. Annotating means you are doing the hard work while you read, allowing you to reference your previous work and have a clear jumping-off point for future work.

1. Survey : This is your first time through the reading

You can annotate by hand or by using document software. You can also annotate on post-its if you have a text you do not want to mark up. As you annotate, use these strategies to make the most of your efforts:

• Include a key or legend on your paper that indicates what each marking is for, and use a different marking for each type of information. Example: Underline for key points, highlight for vocabulary, and circle for transition points.
• If you use highlighters, consider using different colors for different types of reactions to the text. Example: Yellow for definitions, orange for questions, and blue for disagreement/confusion.
• Dedicate different tasks to each margin: Use one margin to make an outline of the text (thesis statement, description, definition #1, counter argument, etc.) and summarize main ideas, and use the other margin to note your thoughts, questions, and reactions to the text.

Lastly, as you annotate, make sure you are including descriptions of the text as well as your own reactions to the text. This will allow you to skim your notations at a later date to locate key information and quotations, and to recall your thought processes more easily and quickly.

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• Last Updated: Apr 25, 2024 2:50 PM
• URL: https://research.ewu.edu/writers_c_read_study_strategies

How to Annotate Texts

Use the links below to jump directly to any section of this guide:

Annotation Fundamentals

How to start annotating , how to annotate digital texts, how to annotate a textbook, how to annotate a scholarly article or book, how to annotate literature, how to annotate images, videos, and performances, additional resources for teachers.

Writing in your books can make you smarter. Or, at least (according to education experts), annotation–an umbrella term for underlining, highlighting, circling, and, most importantly, leaving comments in the margins–helps students to remember and comprehend what they read. Annotation is like a conversation between reader and text. Proper annotation allows students to record their own opinions and reactions, which can serve as the inspiration for research questions and theses. So, whether you're reading a novel, poem, news article, or science textbook, taking notes along the way can give you an advantage in preparing for tests or writing essays. This guide contains resources that explain the benefits of annotating texts, provide annotation tools, and suggest approaches for diverse kinds of texts; the last section includes lesson plans and exercises for teachers.

Why annotate? As the resources below explain, annotation allows students to emphasize connections to material covered elsewhere in the text (or in other texts), material covered previously in the course, or material covered in lectures and discussion. In other words, proper annotation is an organizing tool and a time saver. The links in this section will introduce you to the theory, practice, and purpose of annotation. 

How to Mark a Book, by Mortimer Adler

This famous, charming essay lays out the case for marking up books, and provides practical suggestions at the end including underlining, highlighting, circling key words, using vertical lines to mark shifts in tone/subject, numbering points in an argument, and keeping track of questions that occur to you as you read. 

How Annotation Reshapes Student Thinking (TeacherHUB)

In this article, a high school teacher discusses the importance of annotation and how annotation encourages more effective critical thinking.

The Future of Annotation (Journal of Business and Technical Communication)

This scholarly article summarizes research on the benefits of annotation in the classroom and in business. It also discusses how technology and digital texts might affect the future of annotation. 

Annotating to Deepen Understanding (Texas Education Agency)

This website provides another introduction to annotation (designed for 11th graders). It includes a helpful section that teaches students how to annotate reading comprehension passages on tests.

Once you understand what annotation is, you're ready to begin. But what tools do you need? How do you prepare? The resources linked in this section list strategies and techniques you can use to start annotating. 

What is Annotating? (Charleston County School District)

This resource gives an overview of annotation styles, including useful shorthands and symbols. This is a good place for a student who has never annotated before to begin.

How to Annotate Text While Reading (YouTube)

This video tutorial (appropriate for grades 6–10) explains the basic ins and outs of annotation and gives examples of the type of information students should be looking for.

Annotation Practices: Reading a Play-text vs. Watching Film (U Calgary)

This blog post, written by a student, talks about how the goals and approaches of annotation might change depending on the type of text or performance being observed. 

Annotating Texts with Sticky Notes (Lyndhurst Schools)

Sometimes students are asked to annotate books they don't own or can't write in for other reasons. This resource provides some strategies for using sticky notes instead.

Teaching Students to Close Read...When You Can't Mark the Text (Performing in Education)

Here, a sixth grade teacher demonstrates the strategies she uses for getting her students to annotate with sticky notes. This resource includes a link to the teacher's free Annotation Bookmark (via Teachers Pay Teachers).

Digital texts can present a special challenge when it comes to annotation; emerging research suggests that many students struggle to critically read and retain information from digital texts. However, proper annotation can solve the problem. This section contains links to the most highly-utilized platforms for electronic annotation.

Evernote is one of the two big players in the "digital annotation apps" game. In addition to allowing users to annotate digital documents, the service (for a fee) allows users to group multiple formats (PDF, webpages, scanned hand-written notes) into separate notebooks, create voice recordings, and sync across all sorts of devices. 

OneNote is Evernote's main competitor. Reviews suggest that OneNote allows for more freedom for digital note-taking than Evernote, but that it is slightly more awkward to import and annotate a PDF, especially on certain platforms. However, OneNote's free version is slightly more feature-filled, and OneNote allows you to link your notes to time stamps on an audio recording.

Diigo is a basic browser extension that allows a user to annotate webpages. Diigo also offers a Screenshot app that allows for direct saving to Google Drive.

While the creators of Hypothesis like to focus on their app's social dimension, students are more likely to be interested in the private highlighting and annotating functions of this program.

Foxit PDF Reader

Foxit is one of the leading PDF readers. Though the full suite must be purchased, Foxit offers a number of annotation and highlighting tools for free.

Nitro PDF Reader

This is another well-reviewed, free PDF reader that includes annotation and highlighting. Annotation, text editing, and other tools are included in the free version.

Goodreader is a very popular Mac-only app that includes annotation and editing tools for PDFs, Word documents, Powerpoint, and other formats.

Although textbooks have vocabulary lists, summaries, and other features to emphasize important material, annotation can allow students to process information and discover their own connections. This section links to guides and video tutorials that introduce you to textbook annotation. 

Annotating Textbooks (Niagara University)

This PDF provides a basic introduction as well as strategies including focusing on main ideas, working by section or chapter, annotating in your own words, and turning section headings into questions.

A Simple Guide to Text Annotation (Catawba College)

The simple, practical strategies laid out in this step-by-step guide will help students learn how to break down chapters in their textbooks using main ideas, definitions, lists, summaries, and potential test questions.

Annotating (Mercer Community College)

This packet, an excerpt from a literature textbook, provides a short exercise and some examples of how to do textbook annotation, including using shorthand and symbols.

Reading Your Healthcare Textbook: Annotation (Saddleback College)

This powerpoint contains a number of helpful suggestions, especially for students who are new to annotation. It emphasizes limited highlighting, lots of student writing, and using key words to find the most important information in a textbook. Despite the title, it is useful to a student in any discipline.

Annotating a Textbook (Excelsior College OWL)

This video (with included transcript) discusses how to use textbook features like boxes and sidebars to help guide annotation. It's an extremely helpful, detailed discussion of how textbooks are organized.

Because scholarly articles and books have complex arguments and often depend on technical vocabulary, they present particular challenges for an annotating student. The resources in this section help students get to the heart of scholarly texts in order to annotate and, by extension, understand the reading.

Annotating a Text (Hunter College)

This resource is designed for college students and shows how to annotate a scholarly article using highlighting, paraphrase, a descriptive outline, and a two-margin approach. It ends with a sample passage marked up using the strategies provided. 

Guide to Annotating the Scholarly Article (ReadWriteThink.org)

This is an effective introduction to annotating scholarly articles across all disciplines. This resource encourages students to break down how the article uses primary and secondary sources and to annotate the types of arguments and persuasive strategies (synthesis, analysis, compare/contrast).

How to Highlight and Annotate Your Research Articles (CHHS Media Center)

This video, developed by a high school media specialist, provides an effective beginner-level introduction to annotating research articles. 

How to Read a Scholarly Book (AndrewJacobs.org)

In this essay, a college professor lets readers in on the secrets of scholarly monographs. Though he does not discuss annotation, he explains how to find a scholarly book's thesis, methodology, and often even a brief literature review in the introduction. This is a key place for students to focus when creating annotations. 

A 5-step Approach to Reading Scholarly Literature and Taking Notes (Heather Young Leslie)

This resource, written by a professor of anthropology, is an even more comprehensive and detailed guide to reading scholarly literature. Combining the annotation techniques above with the reading strategy here allows students to process scholarly book efficiently. 

Annotation is also an important part of close reading works of literature. Annotating helps students recognize symbolism, double meanings, and other literary devices. These resources provide additional guidelines on annotating literature.

AP English Language Annotation Guide (YouTube)

In this ~10 minute video, an AP Language teacher provides tips and suggestions for using annotations to point out rhetorical strategies and other important information.

Annotating Text Lesson (YouTube)

In this video tutorial, an English teacher shows how she uses the white board to guide students through annotation and close reading. This resource uses an in-depth example to model annotation step-by-step.

Close Reading a Text and Avoiding Pitfalls (Purdue OWL)

This resources demonstrates how annotation is a central part of a solid close reading strategy; it also lists common mistakes to avoid in the annotation process.

AP Literature Assignment: Annotating Literature (Mount Notre Dame H.S.)

This brief assignment sheet contains suggestions for what to annotate in a novel, including building connections between parts of the book, among multiple books you are reading/have read, and between the book and your own experience. It also includes samples of quality annotations.

AP Handout: Annotation Guide (Covington Catholic H.S.)

This annotation guide shows how to keep track of symbolism, figurative language, and other devices in a novel using a highlighter, a pencil, and every part of a book (including the front and back covers).

In addition to written resources, it's possible to annotate visual "texts" like theatrical performances, movies, sculptures, and paintings. Taking notes on visual texts allows students to recall details after viewing a resource which, unlike a book, can't be re-read or re-visited ( for example, a play that has finished its run, or an art exhibition that is far away). These resources draw attention to the special questions and techniques that students should use when dealing with visual texts.

How to Take Notes on Videos (U of Southern California)

This resource is a good place to start for a student who has never had to take notes on film before. It briefly outlines three general approaches to note-taking on a film. 

How to Analyze a Movie, Step-by-Step (San Diego Film Festival)

This detailed guide provides lots of tips for film criticism and analysis. It contains a list of specific questions to ask with respect to plot, character development, direction, musical score, cinematography, special effects, and more. 

How to "Read" a Film (UPenn)

This resource provides an academic perspective on the art of annotating and analyzing a film. Like other resources, it provides students a checklist of things to watch out for as they watch the film.

Art Annotation Guide (Gosford Hill School)

This resource focuses on how to annotate a piece of art with respect to its formal elements like line, tone, mood, and composition. It contains a number of helpful questions and relevant examples. 

Photography Annotation (Arts at Trinity)

This resource is designed specifically for photography students. Like some of the other resources on this list, it primarily focuses on formal elements, but also shows students how to integrate the specific technical vocabulary of modern photography. This resource also contains a number of helpful sample annotations.

How to Review a Play (U of Wisconsin)

This resource from the University of Wisconsin Writing Center is designed to help students write a review of a play. It contains suggested questions for students to keep in mind as they watch a given production. This resource helps students think about staging, props, script alterations, and many other key elements of a performance.

This section contains links to lessons plans and exercises suitable for high school and college instructors.

Beyond the Yellow Highlighter: Teaching Annotation Skills to Improve Reading Comprehension (English Journal)

In this journal article, a high school teacher talks about her approach to teaching annotation. This article makes a clear distinction between annotation and mere highlighting.

Lesson Plan for Teaching Annotation, Grades 9–12 (readwritethink.org)

This lesson plan, published by the National Council of Teachers of English, contains four complete lessons that help introduce high school students to annotation.

Teaching Theme Using Close Reading (Performing in Education)

This lesson plan was developed by a middle school teacher, and is aligned to Common Core. The teacher presents her strategies and resources in comprehensive fashion.

Analyzing a Speech Using Annotation (UNC-TV/PBS Learning Media)

This complete lesson plan, which includes a guide for the teacher and relevant handouts for students, will prepare students to analyze both the written and presentation components of a speech. This lesson plan is best for students in 6th–10th grade.

Writing to Learn History: Annotation and Mini-Writes (teachinghistory.org)

This teaching guide, developed for high school History classes, provides handouts and suggested exercises that can help students become more comfortable with annotating historical sources.

Writing About Art (The College Board)

This Prezi presentation is useful to any teacher introducing students to the basics of annotating art. The presentation covers annotating for both formal elements and historical/cultural significance.

Film Study Worksheets (TeachWithMovies.org)

This resource contains links to a general film study worksheet, as well as specific worksheets for novel adaptations, historical films, documentaries, and more. These resources are appropriate for advanced middle school students and some high school students. 

Annotation Practice Worksheet (La Guardia Community College)

This worksheet has a sample text and instructions for students to annotate it. It is a useful resource for teachers who want to give their students a chance to practice, but don't have the time to select an appropriate piece of text. 

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1.5: Annotating a Text

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• Page ID 124364

• Gabriel Winer & Elizabeth Wadell
• Berkeley City College & Laney College via ASCCC Open Educational Resources Initiative (OERI)

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"Annotation" means writing notes while you read, usually directly on the text you are reading. While it is common for students to highlight important information in a text, highlighting is considered a passive activity. We want to take notes as we read. Annotating is an important active reading strategy because we engage with a text as the reader. It is as though we are having a conversation with the writer. We might ask them questions, make predictions and connections, or show our agreement or disagreement. We also read a text more closely and retain it better since taking notes slows down our reading process. When it is time to write about a text or take a test, for instance, we will not need to re-read everything, and we can use our annotations instead. Each reader brings their own ideas and background “baggage” to the text, so your annotations will be different from your classmates. Reading is thinking, and like a mirror or a window as in Figure 1.5.1, annotating makes our thinking visible!

Studying an example annotation

Look at the example annotation below. What observations can you make? Discuss with a partner:

Notice this!

First, read the editorial. Think about what parts are important, how the parts connect to each other, and what questions you have.

Editorial from an online magazine: "A Win for Undocumented Immigrants is a Win for All"

Guest Column by Tram Nguyen, Virginia Mercury , March 27, 2020

Every person in Virginia deserves to live in their community free from fear.

The exclusion of undocumented Virginians from being able to drive has been a crippling barrier to that principle. But the state legislature has now passed a bill that will allow driving for many immigrant residents through driver privilege cards this session. At New Virginia Majority we have organized and advocated for this basic right, and now previously undocumented families and communities will legally be able to drive in the state of Virginia.

Having the right to drive can be a matter of life and death under everyday circumstances: a sick child that needs immediate medical attention, or a woman getting ready to bring a new life into the world. Most of us take this access for granted. This is a crucial advancement – for those that have waited patiently just to be able to drive themselves to work, take their children to doctor’s appointments, or attend local events, this a chance to become fully active members of their community.

Ovidia Castillo Rosa, a member of New Virginia Majority’s Loudoun County chapter described it best: ”Not having a driver’s license is like not having feet. Being able to drive would be like having wings. When I have a driver’s license, there are so many things that I’ll have the freedom to do, including starting my own business.”

Cecilia Cruz, a member of the New Virginia Majority, has been involved in the fight for the right to drive, and has called her representatives, and encouraged her friends and neighbors to march in support. “The streets will be more safe and more money will stay in the state,” she said. “Families will be able to leave their children and go to work in safety, without fear.”

Providing this credential will give thousands of Virginians the ability to legally drive and is a huge victory. But we recognize driver privilege cards are not the same as driver’s licenses, and there is much more to be done. Our communities, in an era of open and growing institutional racism and xenophobia, understand that having a driver’s privilege card has the potential to make them vulnerable, as it will make them immediately identifiable as an undocumented person and creates a segment of immigrant drivers.

Our organizers, advocates, and chapter members will continue to fight for a society that treats people equally and with dignity no matter what their status, language, zip code, gender, race, or ethnicity is, and we will work to strengthen privacy protections for all Virginians, regardless of immigration status.

When the presidential election comes to a close in the fall, we pledge to stand by immigrant communities and keep them informed of both the opportunities and threats presented by this law, regardless of who holds the office.

We believe in a Virginia that is welcoming and provides an opportunity for all its residents to succeed and live happy and healthy lives. The outcome of this legislative session was a step in the right direction and toward a more inclusive Virginia. But until all of our communities are granted the full protections and access to driver’s licenses, the fight continues.

Tram Nguyen is co-executive director of New Virginia Majority, which works to builds power in working-class communities of color, in immigrant communities, among LGBTQ people, women, youth, and progressives across the commonwealth.

"A Win for Undocumented Immigrants is a Win for All" was originally published in the Virginia Mercury and is licensed under CC BY NC ND.

Now look at Figure 1.5.2, an annotation of the same text. What do you notice? What else would you add? Discuss with a partner.

Figure 1.5.1 shows the text of "A Win for Undocumented Immigrants is a Win for all" by Tram Nguyen with some passages highlighted, with notes that summarize key information, ask questions, and respond to wording choices the writer made.

See 1.5.1 for an accessible version of the above model annotation . 

Active readers use annotation as a way to

• Make predictions
• Ask questions and look for answers
• Visualize (make pictures in our mind or draw an image or diagram)
• Show agree/disagreements
• Identify problems and/or solutions
• Make connections to ourselves (our background, values, etc), other texts (articles, books, movies, etc), or the world (news events, politics, etc)
• Mark key points
• Summarize key points/ sections of the reading
• Make note of information that shocks, surprises, or challenges us and our beliefs
• Identify unfamiliar vocabulary or parts that are unclear

Some readers also like to

• underline or use symbols to point out key information
• write key words in the margin
• circle definitions and meanings
• write questions in the margins where answers can be found
• identify steps with numbers
• draw arrows to show relationships
• write short summaries in the margins

Practicing annotation

Now let's use active reading strategies and annotation with a short academic article.

• Use the active reading strategies to get an overview of this article.
• Annotate as you read the article, “Undocumented Immigrants May Actually Make American Communities Safer – Not More dangerous – New Study Finds.”

Reading from an online magazine: Undocumented immigrants may actually make American communities safer – not more dangerous – new study finds

Robert M. Adelman, University at Buffalo and Lesley Reid, University of Alabama

The Research Brief is a short take about interesting academic work.

The big idea

Undocumented immigration does not increase the violent crime rate in U.S. metropolitan areas. In fact, it may reduce property crime rates. These are the key findings from our recently published article in the Journal of Crime and Justice, co-authored by Yulin Yang, James Bachmeier and Mike Maciag.

Research shows that the American communities where immigrants make their homes are more often improved by their presence than harmed by it. Immigrants bring social, cultural and economic activity to the places they live. That makes these places more vital and safer, not more dangerous.

Why it matters

People from all social groups and backgrounds commit crimes. But undocumented immigrants, and immigrants more generally, are often baselessly blamed for increasing crime rates – including, repeatedly, by President Donald Trump. In the second and final presidential debate, Trump again claimed undocumented immigrants are rapists and murderers.

This notion has existed and been studied since the early 20th century, including in a 2005 analysis we conducted with a number of colleagues that concluded immigration did not increase crime rates in U.S. metropolitan areas.

But this research is often dismissed because most empirical studies cannot separate undocumented immigrants from the total immigrant population. That level of analysis is necessary to draw conclusions about the relationship between undocumented immigration and crime.

For example, we found in a 2017 study with colleagues that from 1970 to 2010 metropolitan areas with greater concentrations of immigrants, legal and undocumented combined, have less property crime than areas with fewer immigrants, on average. Critics suggested that our findings would not hold if we looked at only the subset of undocumented individuals.

So we decided to find out if they were right. Our new study is the result of that effort, and it confirms our original findings: Undocumented immigration, on average, has no effect on violent crime across U.S. metropolitan areas.

In statistical models that did identify a significant relationship between undocumented immigration and crime, we found undocumented immigration reduces property crimes, such as burglary.

How we do our work

Using two different estimates of the undocumented immigrant populations for 154 metropolitan areas in our most recent study – one from the Pew Research Center and one from the Migration Population Institute – we examined the effect of undocumented immigration on homicide, aggravated assault, robbery, burglary and larceny crime rates.

Crime rate data came from the FBI’s Uniform Crime Report program. Other data were from the U.S. Census Bureau.

Using a statistical method called regression analysis to examine the data, we found that as the size of the undocumented population increases, the property crime rate decreases, on average. And the size of the undocumented population in a metropolitan area tends to have no impact on the violent crime rate.

These findings build on the conclusions of a large 2018 study in which researchers Graham Ousey and Charis Kubrin examined 51 studies on immigration and crime published from 1994 to 2014.

What still isn’t known

Robert M. Adelman, Associate Professor and Department Chair of Sociology, University at Buffalo and Lesley Reid, Professor of Criminology and Criminal Justice and Interim Dean of the School of Social Work, University of Alabama

This article is republished from The Conversation under a Creative Commons license. Read the original article .

Reflect on your annotations

Discuss your annotations with a partner or in small groups. What observations can you make? What worked well? What was confusing? In what way did your annotations reinforce your learning? What kinds of annotations did you apply the most from the list below? Which types of annotations did you not try? Why do you think that is? What do you plan to do differently when you annotate again?

• Visualize (make pictures in your mind or draw an image or diagram)
• Show agreements or disagreements
• Make connections to yourself (your background, values, etc), other texts (articles, books, movies, etc), or the world (news events, politics, etc)
• Summarize key points or sections of the reading
• Make note of information that shocks, surprises, or challenges you and your beliefs
• Identify parts that are unclear

Works Cited

Adelman, Robert M. and Lesley Reid. "Undocumented Immigrants May Actually Make American Communities Safer – Not More Dangerous – New Study Finds." The Conversation, 27 Oct. 2020

Licenses and Attributions

Cc licensed content: original.

Authored by Marit ter Mate-Martinsen, Santa Barbara City College. License: CC BY NC.

CC Licensed Content: Previously Published

"Undocumented Immigrants May Actually Make American Communities Safer – Not More Dangerous – New Study Finds" by Robert M. Adelman and Lesley Reid. License: CC BY ND.

You likely read, and perhaps also write, annotation every day. Annotation influences how we interact with texts across everyday contexts. Annotation provides information, shares commentary, sparks conversation, expresses power, and aids learning. This is why annotation matters.

1: Introduction

And if you have managed to graduate from college without ever having written ‘Man vs. Nature’ in a margin, perhaps now is the time to take one step forward. —Billy Collins, Marginalia

All the News that’s Fit to Annotate

On April 18, 2019, a redacted version of the Report On The Investigation Into Russian Interference In The 2016 Presidential Election , by Special Counsel Robert Mueller, was released to the public by the U.S. Department of Justice. Annotation shaped how the report was shared and interpreted.

Approximately a tenth of the entire report was redacted, or blacked out . Redaction is a type of annotation. To provide a rationale for extensive redaction, the Department of Justice also annotated each redaction according to one of four color-coded categories. According to an analysis by The New York Times , about 70 percent of the line-by-line redactions concerned ongoing investigations (white annotation), almost 20 percent related to grand jury material (red), and the remaining redactions concerned either classified information, such as investigative techniques (yellow), or personal privacy (green).

Figure 1: Mueller Report

In addition to the report’s redaction-as-annotation and annotation-of-redactions, media reporting of the report’s conclusions about possible coordination (popularly referred to as “collusion”) and obstruction of justice did more than offer a summary of key findings. Journalists annotated the report to provide their readers with information, analysis, and commentary. The Washington Post published a page-by-page analysis titled “The Mueller report, annotated,” NPR offered “Highlights from the Mueller Report, Annotated,” and Politico reporters contributed “An annotated guide to the redacted Mueller report,” among other examples. Annotation across these publications varied in detail, scope, visual style, and interactive features.

Over the past few years, leading media organizations have embraced an annotated approach to journalism. Annotation by reporters frequently accompanies political speeches, debate and interview transcripts, the release of legal documents like the Mueller report, and analysis of news conferences. Why the trend? On the one hand, annotation is easy to feature because it’s similar to established journalism practices, like quoting experts, hyperlinking to supporting resources, and presenting media content. On the other hand, annotation goes a step further by illustrating both granular detail and germane context. Annotation allows journalists to comment more transparently, to more informally share behind-the-scenes or insider perspectives. Annotation is also proving to be an effective fact-checking strategy. 1

Journalistic interest in annotation is not confined to politics. Following Ta-Nehisi Coates’ successful turn authoring Marvel’s Black Panther comic, The New York Times published “Captain America No. 1, by Ta-Nehisi Coates, Annotated.” This online article featured exclusive previews of the comic, insider commentary by Mr. Coates as head writer, and a few spoilers for good measure. 2 In 2017, the Times also featured the author Margaret Atwood annotating key episodes and scenes from the TV adaption of her celebrated novel The Handmaid’s Tale . Yet in both form and function, how different is annotation in The New York Times Magazine’s “Talk” interviews, a featured introduced in 2019, from Listrius’ annotation of Erasmus’ Moriae encomium , “a standard appendage to the work” since the 1515 Basel edition? 3

That which is fit to print - be it the news, or social commentary, or religious doctrine - has for centuries been fit for annotation, too. While it’s not newsworthy to observe that journalism is changing rapidly in our digital era, it is distinctive to note how annotation traditions and conventions are reinvigorating journalism as more connected, more interactive, and more relevant.

The ways in which, and the reasons why, journalists annotate represents but one small set of practices within the broader genre of annotation. Marginalia thrived in England during the sixteenth century, as studies of book culture during the rule of Elizabeth I and James I demonstrate. 4 Annotated books were routinely exchanged among scholars and friends as “social activity” throughout the Victorian era. 5 Some of the most significant commentary about the Talmud, first written in the eleventh century, has been featured prominently as annotation in print editions since the early 1500s. Today, scientists’ annotation of the human genome and proteome for large-scale biomedical research relies upon techniques that are both similar to and also very different from linguists and historians who have translated, annotated, and digitally archived Babylonian and Assyrian clay tablets. 6 From the annotatio of Roman imperial law to the medieval gloss , annotation nowadays helps people to write computer code, evaluate chess games, and interpret rap lyrics.

Perhaps annotation has already appeared in this book, too. Annotation is a form of self-expression, a way to document and curate new knowledge, and is a powerful means of civic engagement and political agency.

Annotation provides information, making knowledge more accessible. Annotation shares commentary, making both expert opinion and everyday perspective more transparent. Annotation sparks conversation, making our dialogue - about art, religion, culture, politics, and research - more interactive. Annotation expresses power, making civic life more robust and participatory. And annotation aids learning, augmenting our intellect, cognition, and collaboration. This is why annotation matters.

You likely read, and perhaps also write, annotation every day. Whether handwritten or digital, this book will help you to define, identify, and author annotation. More importantly, this book will discuss five essential purposes of annotation that contribute to cultural, professional, civic, and educational activities. Annotation is written within the warp and weft of our texts, patterning the fabric of daily life. This book will help you to understand how that happens and why that matters. We’ll start by introducing a key idea that appears throughout this book - annotation is an everyday activity.

An Everyday Activity

The Scottish author Kenneth Grahame, best known for his novel The Wind in the Willows , observed in an 1892 essay, “The child’s scribbling on the margin of his school-books is really worth more to him than all he gets out of them.” 7 Imagine your high school literature course. Or picture those classic scenes from Dead Poets Society . Maybe your English teacher assigned Toni Morrison’s Beloved . While reading, perhaps you highlighted key passages, noted plot devices, and commented on structure and dialogue. How else to comprehend two chapters, back-to-back, famous and famously unconventional, both of which begin “I am Beloved and she is mine.”? You might have shown these annotations to your teacher - “you see, I did the reading!” - or used them as references when writing a final paper. Or do you recall jotting down formulae for molecules and compounds in the margins of your chemistry textbook? Writing to students in 1940, the American philosopher and educator Mortimer Adler declared, “Marking up a book is not an act of mutilation but of love.” 8

What is everyday about annotation in school? Annotation may have been an expected or required academic practice. You’ve likely annotated as a part of many different courses and inside many different texts. Maybe annotation in school helped you to develop an idiosyncratic notation system that you still use today. Or maybe you were taught a more formal convention. Annotation happens every day in school and is an everyday activity for students, for “at every stage, students working with books have used the tool of annotation.” 9 And graduation from high school or college probably didn’t get you off the hook; as we’ll discuss, annotation is an everyday activity for many professionals, like journalists, programmers, scientists, and scholars.

But we’re not all scientists or scholars who annotate as a part of our job. Let’s bring this idea of everyday annotation a bit closer to home. Some of us may fondly recall measuring the height of a child against a doorframe, making a small pencil mark, and then writing down your child’s name and the date. Or maybe you were that child and this family ritual also helped you to practice writing your name while growing up. As you, or your child, aged, so too did these measurement marks travel upwards, edging ever closer to the upper frame. This, too, is an act of everyday annotation, stretched over time and etched with love. While this type of annotated measurement might not have happened every single day, it did record an aggregate of day-to-day changes. And the annotation - the marks, the names, the dates - served as a visual and daily reminder of growth added to the text and texture of life.

Annotation is an everyday activity whether it makes journalism more viable or schooling more valuable. Annotation is an everyday activity whether it contributes to scientific discovery or catalogues a child’s growth. Annotation is an everyday activity because different types of notes, whether political commentary or a child’s name, are added by many different types of people - journalists, programmers, parents and children - to a variety of texts, like transcripts, and code, and even a door jamb. In these cases, and in many others discussed throughout this book, it isn’t our prerogative to suggest which examples of everyday annotation are more or less remarkable (and yes, pun intended). Rather, it is our job to highlight how these various types of annotation share five common purposes - to provide information, to share commentary, to spark conversation, to express power, and to aid learning. Annotation makes knowledge more accessible, perspective more transparent, discourse more interactive, authority more contested and complex, and education more vibrant.

Defining Annotation

As print culture developed in Renaissance Europe and books become more widely available to a reading public, so, too, did “marginal material” flourish and serve various purposes. During the 1500s, annotation added details and examples to books, and provided references, corrected or objected to an author’s statement, emphasized importance, evaluated arguments, provided justification and translation, and even parodied the text, among other functions. 10 It wasn’t until 1819 that Samuel Taylor Coleridge first used the term “marginalia,” from the Latin marginalis (or “in the margin”), when, as a literary critic, he wrote about another author’s work for Blackwood’s Magazine . 11 Were you to ask more contemporary scholars about annotation, they might suggest annotation facilitates reading and later writing, the ability to “eavesdrop” on other readers, that annotation provides feedback and opportunities for collaboration, and “call[s] attention to topics and important passages.” 12 And when Sam Anderson wrote his 2011 essay What I Really Want Is Someone Rolling Around in the Text , he recalled experiencing annotation as additive, useful, social, a means to collaborate with a text, and as “meta-conversation running in the margins.” 13 Then again, a computer scientist will tell you that annotation means labeling data - images, text, or audio - for the purposes of identifying, categorizing, and training machine learning systems.

So how to define annotation? Merriam-Webster defines annotation as “a note added by way of comment or explanation.” And the Oxford English Dictionary echoes with: “A note by way of explanation or comment added to a text or diagram.” In this book, we’ll take an even simpler approach and define annotation as:

A note added to a text.

We’ve settled upon this definition because it gives us flexibility to explore the broad genre of annotation, both handwritten and digital, textual and visual, from different periods of time, and that serve different cultural and civic purposes. You might notice that unlike the standard-bearing dictionaries, our definition does not include the terms “comment” and “explanation,” denoting two annotation purposes. And that’s because, in our assessment, annotation serves five equally important, and sometimes overlapping purposes: Providing information, sharing commentary, expressing power, sparking conversation, and aiding learning. Our definition of annotation will allow us to explore wide-ranging issues of authorship, intent, and expression.

Most immediately, our definition of annotation requires us to ask - and answer - three questions. First, what is a note? Second, what does it mean to add? And third, what is a text?

Figure 2: A note added to text

What is a Note?

Notes , according to literary scholar Andrew Piper, are “records of the quotidian.” 14 In our earlier examples of everyday annotation, we featured various types of notes. A note can be a word, phrase, sentence, or even extended prose written in a textbook or cookbook. Some scholars have argued that signs and nonverbal codes are not notes, that notes must be discursive and responsive. 15 However, to embrace the full repertoire of annotation we also suggest that a note can be a symbol, like a question mark, exclamation point, or an asterisk. Copyediting marks are notes, and so too tick or tally marks like those added to the Lee Resolution, or The Resolution for Independence, passed by the Second Continental Congress on July 2, 1776. The Lee Resolution features 12 marks tallying the “united colonies” that voted for American independence.

Figure 3: Lee Resolution

Notes help mediate the relationship between reading and writing. For Piper, notes are “silent embers,” indicating “where the often mind-numbing, repetitive mundaneness of our daily lives bump into the high-flying acrobatics of human intellect.” 16 In 1947, a high-flying moth bumped into Harvard’s Mark II Aiken Relay Calculator, was removed by computer operator William Burke, and then taped to the computer log - popularizing the existing term “debugging.”

Figure 4: debugging

To discuss what counts as a note and how notes work, we need to introduce a new idea - multimodality .

With roots in rhetoric and semiotics, the concept of multimodality has long influenced how we understand and participate in acts of communication. A full theoretical review of multimodality is beyond the scope of this book. For our purposes, it will be sufficient to start by explaining how different forms of media are characterized by different types of modes . Earlier, we mentioned Toni Morrison’s Beloved . Beloved is a book, and books are a category of media. In the case of Beloved , this media artifact uses a textual mode of communication between Toni Morrison, the author, and you, the reader. Of course, media in the same category can feature different modes. Children’s picture books and your coffee table book featuring landscape photography exemplify a visual modality, whereas books written in braille demonstrate the importance of a tactile mode.

To further understand the relationship between media and mode, let’s briefly revisit two examples that we’ve already mentioned. Margaret Atwood’s novel The Handmaid’s Tale has been narrated by the actress Claire Danes to create an audiobook, another type of media, which allows for communication with listeners via an aural modality. Ta-Nehisi Coates, the lead writer of Black Panther , worked alongside a team of illustrators and colorists who, together, created a series of comic books (yet another type of media) that communicated with readers through both textual and visual modalities. Furthermore, both The Handmaid’s Tale and Black Panther demonstrate how a specific text can be adapted from one type of media to another; the former from a book into a television show, the latter from comic books into a feature film. And when that happened, the primary modality of both texts also changed to favor a visual mode.

Just like different types of media communicate through various modes, so, too, do notes communicate through multiple modes. As records of the quotidian, words, symbols, images, and even animated GIFs may all be notes that communicate through a textual mode, or a visual mode, or an aural model. When we discuss what counts as a note in this book, and when we describe how notes function in relation to a text, we do so with multimodality in mind. Annotating Beloved in literature class likely meant adding textual notes to a book that also communicated through a textual mode. Alternatively, adding textual notes to an image, or vice versa when adding a doodle or an image atop a text, suggests that the concept of multimodality - or what Piper describes as the “multidimensional” qualities of notes - will help us to examine notes and their relationship to annotation.

Consider all the various media you interact with every day - books, newspapers, magazines, and comics (including these texts’ digital versions), as well as movies, video games, podcasts, and social media. Your daily media diet likely features various modalities: Textual, for the media you read; visual, for media you watch; tactile, for media you touch, like touch screens at a restaurant or museum; and aural, for your favorite podcasts. Have you ever noticed annotation associated with this media? If so, what do these notes say and through what modalities do they speak? If, as Piper suggests, notes are “technologies of oversight,” then as multiple notes are added to a text over time, it’s likely that a group of notes will come to demonstrate the multimodality and multidimensionality of annotation. Importantly, notes are not only written; notes, in our view, may be more than words.

What Does it Mean to Add?

We now understand what notes are, how notes function, and that notes are multimodal. Let’s survey a few records of the quotidian in a variety of everyday scenarios. You’re reading a favorite book, jotting words and symbols in the margins; this is your “private exchange” with the author, a means of “talking back to” the text. 17 An educator reads a student’s essay and returns it covered in red-inked copyediting marks, or does the same using a digital annotation application. You and a colleague use collaborative word processing software to write a report for colleagues in your organization, a version of augmented intellectual work anticipated by, among others, the inventor and Internet pioneer Douglas Engelbart in the early 1960s. 18 In these instances, we recognize the importance of idiosyncratic meaning-making, how a student’s first draft is improved through expert feedback, and how a report is co-authored thanks to professional processes. The addition of notes while reading a book, revising a school assignment, or authoring a report communicates an important message: To add a note is to act with agency.

The word agency is traced back to the Latin verb agō meaning to act, to do, or to make. In our discussion of annotation we’re not referring to the type of organizational agency that makes stuff, like an advertising firm or a government bureaucracy. Rather, we’re interested in how an individual or a group acts and makes stuff, as when copy editing an essay or collaboratively writing research. And when doing so, it’s likely that people will annotate, that they will add some type of note to a text, that they will author a “responsive kind of writing permanently anchored to preexisting written words.” 19 Our expansive view of annotation suggests notes are not only responsive writing (notes might be GIFs), and also that more than written words are annotated (buildings can be annotated, too). What really matters, in this discussion of agency, is the fact that when a note is added - when people exercise agency in different contexts, under a variety of circumstances, and for many purposes - an annotation is permanently anchored to a text.

What does it mean to add? Adding is to act with agency. When we discuss annotation in this book, agency means that someone has permanently anchored a note to a text.

What is a Text?

This book you’re reading - whether printed on paper and bound together, or as a digital epub - is a text . We’ve mentioned a lot of texts so far: Beloved , The Handmaid’s Tale , Black Panther . We began our introduction by referencing multiple news articles written by journalists and published by media organizations; those articles are also texts. If you’ve made it this far into a book about annotation, then you’re likely familiar with a diversity of material and digital texts. In forthcoming chapters we’ll discuss medieval manuscripts, religious scripture, works of art, hashtags, computer code, legislation, and all manner of images - they’re all texts, too.

What makes something a text? First, a text has an author. Someone, or maybe a group of someones, authors a text by writing, composing, speaking, drawing, or through photography. Second, a text is defined by its content. A text conveys a “main message.” Some would say that texts have a “body,” and that texts are distinguished by a given style or subject. As we’ve discussed, texts are also defined by different forms of media; one text is a comic book, another a film. And texts communicate messages through different modes including, as with comic books, multiple modalities at the same time. While different types of annotation like marginalia, glosses, and rubrication have historically appeared as notes within books (as we’ll discuss in Chapter 2), the breadth of what we count as a text is a reminder that annotation may be permanently anchored to much more than books.

The features of a text should resonate as familiar given something you’ve likely authored today, if not quite recently - a text message. You’re the author of the text message. To author your text message, perhaps you wrote words, or used an emoji, or included a photograph or GIF. The content, or the body, of your text quite literally conveys a message: “Here’s the book I recommended,” or “Let’s go see a movie this weekend.” And depending upon whether you composed with words, or with emojis, or with images, your text message communicated through a textual mode, via a visual mode, or maybe with multiple modalities.

Why discuss the qualities of texts and this example of text messages? For two reasons. First, the defining features of a text - an author, message, structure, and style - are similar to the features of notes - which are quotidian, include both words and signs, and are multimodal. Both texts and notes can take the form of different media and communicate through different modalities. The second reason is this: the interplay of a note added to a text can best be understood by introducing the concept of intertextuality .

Intertextuality

Intertextuality is essential to further articulating annotation as a note added to a text.

Simply put, intertextuality describes the relationship between texts. A relationship between two (or more) texts might be established for the purposes of comparison, or alliteration, or interpretation, or as a means of fact-checking or critique. In some cases, an intertextual relationship may be explicit. This book, so far, has referenced Beloved and Black Panther on multiple occasions to help illustrate key ideas. In doing so, we’ve begun building a series of intertextual dialogues among these texts. In other instances, an intertextual relationship may be implicit. For example, there is both implicit and interpretive intertextuality among James Joyce’s Ulysses and Homer’s Odyssey , as well as between the Odyssey and the Coen brothers’ film O Brother, Where Art Thou?

Of course, the idea of intertextuality is a bit more complex than just explicit or implicit references among texts. The Russian philosopher and literary theorist Mikhail Bakhtin, writing throughout the 1930s and 40s, suggested that the nature of language is dialogical . He argued that both written and spoken language is always in dialogue with other texts and authors. We agree with Bakhtin’s view. What you write and what you say is dialogical because it’s responsive to other people (like a teacher or colleague), other texts (like the Odyssey ), as well as other ideas (as when, for example, you hold a sign or chant during a protest). And because we embrace the idea that written and spoken language is dialogical, we can now suggest that annotation - the addition and permanent anchoring of a note to a text - is dialogical, too.

Not only is annotation dialogical, we can also observe that all annotation is intertextual. And rather than take our word for it, let’s be expressly dialogical and put our book and ideas into dialogue with another text and set of ideas. Figure 4 includes a quote by the French philosopher Jacques Derrida. In addition to this quote, we’ve exercised our agency - to express our authorial power, to spark a response from you - by annotating Derrida. 20 Here, in dialogue with one another, is what we have to say about intertextuality and annotation. And maybe you, too, have something to say.

Figure 5: Derrida

So what does Derrida mean by annotation helping to prop up one discourse on another? Let’s revisit a few of the examples we’ve already discussed. When journalists annotated the Mueller report, annotation established an intertextual relationship between the report (as one “discourse”) and other discourses or dialogues, such as referenced evidence, an expert’s analysis, or established fact. When you annotated Beloved in a literature course, you created an intertextual relationship between Toni Morrison’s book (her discourse) and your own discourse comprised of reactions and wonderings. And your annotation of Beloved also added marks of evidence for when, subsequently, you were in dialogue with peers and your teacher. Just as annotation is multimodal, so too is it intertextual.

Annotation in Action

We’ve now established that annotation is an everyday activity. It’s likely that you read and write annotation regularly, perhaps on a daily basis. We’ve also introduced the ideas of multimodality, agency, and intertextuality, and suggested that the act of adding notes to a text is both multimodal and intertextual. As we close this introductory chapter, we’ll do so by sharing one more example of annotation in action. And rather than reference other texts or present hypothetical scenarios, we’ll turn our gaze inward and describe this very book.

Annotation was integral to how we wrote and received feedback about Annotation , how The MIT Press published the book, and how, perhaps, you’re reading and responding to the book right now.

The first full draft of our manuscript was shared publicly for the purposes of open peer review by The MIT Press using the online publication platform PubPub. Throughout the summer of 2019… [ADD SUMMARY OF OPEN PEER REVIEW PROCESS, INCLUDING TOTAL PARTICIPANTS AND ANNOTATION, EXAMPLES, ETC.].

This book, in its published form, also features various forms of annotation. The MIT Press has established a number of structural conventions that are consistent across all the books published as part of the Essential Knowledge series. As you may have already noticed, this book includes a Notes section just before the Bibliography (it starts on page XXX). This section of Notes is a collection of annotations. Organized by chapter, Notes presents to you, the reader, a total of 208 endnotes that we felt were important to add to the body of our text (even if, at times, it’s difficult flipping back to these endnotes rather than reading more proximal footnotes 21 ). In addition to Notes, this book includes a Glossary of key terms (see page XXX). We’ll discuss the origin and purpose of glosses and glossaries next, in Chapter 2. All glossaries are a curated list of annotations, and that goes for the Glossary in this book, too. Finally, just before the Index, this book includes suggested Further Readings. As researchers of literacy and learning, we’ve read and written quite a bit about annotation. We’ve selected a list of readings that we hope you might put into further dialogue with our book as you continue to explore annotation. In appending these Further Readings to this book, we’ve added yet another note to this text. Together, the Glossary, Notes, and Further Readings provide three different types of annotation that are integral to this text, adding both structure and insight for you, our reader.

And speaking of our readers, what of your annotation? Have you underlined or circled a word or phrase? Have you written an interlinear annotation, such as a word or symbol between the lines of text? Or have you added marginalia, a responsive type of discourse? And have you cursed us out, or called us names, or disagreed vehemently with our ideas? If you’ve yet to do so, now might be the time! Maybe you’ve borrowed this book from a friend. Marking up this book makes your thinking visible, allowing your friend to someday see what you thought about annotation and how you responded to our ideas. Or maybe you’re reading this book for a class or as part of a research project. Annotating this introductory chapter about annotation may help you to experience multimodality, exercise agency, establish intertextual relationships, and expand dialogical language among other texts, people, and ideas.

We don’t believe a published book is meant to live as a pristine artifact unadulterated in perpetuity. As Mortimer Adler wrote 80 years ago, the marked up book is the “thought-through book,” for it is “a conversation between you and the author.” And we certainly haven’t written the final word about annotation. We’ve hopefully written some useful words about an important topic so as to start a dialogue. We welcome your words and annotation throughout, about, within, and atop this text, too.

Figure 6: Dialogue

Why is Annotation Essential?

Annotation is essential because we interact with a variety of texts everyday, texts that are both digital and material, and texts that communicate through various modalities and may also be multimodal. We interact with these texts dialogically by adding our own thoughts, questions, reactions, and reminders. Annotation is also important because our intertextual interactions with texts cross multiple contexts - the personal, the academic, the professional, and even the commercial and civic: “Printed marginalia, functioning at their most creative level, open doorways specifically, insistently for the purpose of crossing the text-context threshold.” 22 Annotation follows us into, and then changes because of, the different ways in which we interact with texts across everyday contexts.

Annotation matters because it provides information; knowledge becomes more accessible because of annotation. Annotation matters because it shares commentary; perspectives become more transparent because of annotation. Annotation matters because it sparks conversation; dialogue becomes more interactive because of annotation. Annotation matters because it expresses power; authority becomes more contested and complex because of annotation. And annotation matters because it aids learning; education becomes more vibrant because of annotation. We’ll be discussing all of this, and more, as we explore how and why notes are added to texts.

One might also introduce the term “intratextuality” as distinguished from “intertextuality.” The former refers to cross-references within a single text, whereas the latter involves references between or among different texts. Annotation could involve both, I think. A note as such is intratextual … it is anchored to one specific text. But that note could itself contain a reference to another text, thereby introducing intertextuality. And, of course, that further text might be made accessible through a link, giving us the category of hypertextuality.

Uses of annotation in journalism

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Let me start this part for you.

“For example, the annotated contributions of Dr. Jeremy Dean, to whom we dedicate this book…”

While I use this line myself all the time (as salesman essentially), your repetition of the idea has finally given even me pause: there as so many assumptions in this idea that everyone annotates. While it’s likely true for the readers of an MIT Press book and maybe fine as such, there are obviously many people who don’t and for reasons that often have to do with power. Just one example is that some public school teachers often don’t allow students to write in the margins of their books because they will be collected and reused.

Should this section be above the one on note and add?

I think it’s interesting to bring in social media behavior here. What is Instagram but “everyday” visual notes on our lives?

I wonder if the above three paragraphs are really necessary to get here.

Great quote!

is document a strong enough word here? create?

Footnote needed?

I wish I could annotate directly on top of the images you have created but I don’t seem to be able to do that (no fault of yours .. just an observation). If I want to digitally annotate your images (like the one below), I need to move outside of the text and then share back into the text …

It’s strange to read this in the present, with echoes of the future, reaching back to the past. Of course, it has to be written that way, but it feels, here in the margins, like some odd time ripple. I am here, writing about what you say has already been written (and I may even be too late in the process). Time for more coffee …

This seems important — that the reader has a role in the text with annotation … either by themselves or with others.

Ah, yes, here’s mention of the orality component. Could be cool to circle back to the historical side of things to this end. When it comes to discussions of Western spiritual texts (particularly the Torah in Jewish tradition), “oral” annotation, so to speak, was considered far more sacred than written annotation (especially since you can’t write on Talmudic scrolls)

Maybe this will come later in the book too, but I’m wondering whether there is room for discussion here on how often annotation is explicitly taught in schools? To what extent do teachers actively ask their students to engage in annotation activity? Or do students pick up annotation in a more idiosyncratic way?

More accessible to whom exactly? And where does the concept of accessibility, from a disability perspective, fit in here when we talk about annotation?

Great question …

I wonder whether perhaps this section or at least your proposed definition of annotation might not be more useful if it occurred earlier in a reader’s experience? Shortly before getting here I went back and scoured the first sections of chapter 1 and the preface in search of a foundational definitional of annotation and wasn’t quite sure I could offer a working definition for your crucial term as you wanted me to understand it!

It also serves as an outlet for dissent & disagreement, even if only privately!

Great visual example from Wikimedia :

Babylonian Talmud, Seder Zera'im, Venice: Daniel Bomberg [1543-44].

Great visual example from the Babylonian Talmud here: https://upload.wikimedia.org/wikipedia/commons/thumb/3/3b/Babylonian_Talmud%2C_Seder_Zera%27im.jpg/800px-Babylonian_Talmud%2C_Seder_Zera%27im.jpg

This feels a bit more optimistic that I think is warranted. Annotation in journalism seems to me to trending toward very limited, expert-only voices interacting with texts. It might be helpful to balance this trend against a reflection on the rise and decline of ‘comment sections’ on news and other public websites (lots of sites have killed public comments/annotation in the last 5 years), and the relationship between bottom of the page comments (‘page notes’) and anchored annotation/marginalia? See https://www.niemanlab.org/2015/09/what-happened-after-7-news-sites-got-rid-of-reader-comments/ + https://www.americanpressinstitute.org/publications/dropping-comment-sections/ + https://www.kqed.org/lowdown/29720/no-comment-why-a-growing-number-of-news-sites-are-dumping-their-comment-sections + https://www.nytimes.com/roomfordebate/2016/04/18/have-comment-sections-on-news-media-websites-failed + https://www.salon.com/2018/11/17/why-comments-sections-must-die/ + https://www.theatlantic.com/letters/archive/2018/02/letters-comments-on-the-end-of-comments/552392/ + https://www.wired.com/2015/10/brief-history-of-the-demise-of-the-comments-timeline/ + http://audreywatters.com/2017/04/26/no-annotations-thanks-bye

https://www.nytimes.com/2017/06/14/watching/the-handmaids-tale-tv-finale-margaret-atwood.html

https://www.politico.com/story/2019/04/18/mueller-report-summary-key-findings-1280879

https://www.npr.org/2019/04/18/708965026/highlights-from-the-mueller-report

https://www.washingtonpost.com/graphics/2019/politics/read-the-mueller-report/

In particular, the technologies that we have at our disposal for accessing — and annotating — texts matter a great deal. Even if we have robust tools, we may not always know how to use them in the most productive ways, or to communicate our annotations to a wider audience.

Yet, in many ways, they still do… even in our e-book, editable, and constantly interactive world of texts, eventually a book has to stand alone in the world.

One reader’s interpretation of that book (via annotation) is a unique and beautiful act, and many readers can discuss the book, but the book (which could be revised later) still stands alone.

Going to your point above that some works of literature/film can have “implicit and interpretive intertextuality,” I don’t know that my current practice of annotation on your manuscript is something I would put into this intellectual register.

That is, I am annotating, but I don’t know that I am really creating the kinds of intertextuality described by literary theorists. I am making some links, adding some images, and sharing my own ideas, yes. But, I am not writing another book here, making tacit or overt references to your ideas, or mimicking your structure or style.

All this is to say, I think that you might want to make your argument on intertextuality a little more nuanced… are there different “levels” of intertextuality that happen, depending on the quality and type of annotation?

>But, I am not writing another book here… Why not? How do you know!?

Keeping with my thread from above, if students are being told that they must annotate… I wonder if they are truly acting with agency, and engaging in genuine work of annotation. Or, are they merely fulfilling their assignment?

In short, I am afraid that the practice of having students use reading strategies to approach texts, while useful in many ways, can become a mindless exercise filled with many, many sticky notes and few genuine interactions with the text itself.

Agree with the above.

I wonder though if just complicating our understanding of agency and power in these annotation contexts might allows the focus on agency to remain, just complicated.

Another complication: the various platforms in which annotation can happen are themselves not neutral. Even the margin of a page of text has certain limitations and expectations…

Perhaps an additional pair of questions: By whom? For what purpose?

Yet, for decades, students have been explicitly told not to damage or otherwise deface their textbooks (see page 17 in this PDF of a district policy ).

While I can agree that annotations in school are, for some students, common practice, I would encourage you to be more nuanced here.

For some students, who are willing and able to take the teacher’s shared notes, outlines, or slides (or, go so far as to make photocopies of their textbooks, they might engage in the kinds of personal and useful annotation practices you describe.

For the vast majority of students, however, I would argue that annotation (if done at all) is perfunctory. They are given specific texts and tasks, and required to make so many notations in trade for a grade.

The “required” part of your definition, then, could use some elaboration and, to extend the idea, some clarification on whether or not the process of annotating is, ultimately, useful for these students.

Moreover, connecting to your next idea, how can we help students learn to annotate in a way that honors and extends their own “idiosyncratic notation system[s]” in productive, engaging ways?

Seconding Troy’s thoughts here. It would also be interesting to consider the role of medium here and how that’s considered in schools (if at all). That is, annotation itself as a practice may be very different in print and in digital spaces. Is education a space where that nuance is approached? (Not sure if this is necessarily the right place for this particular conversation, but I think there’s more nuance for the school-based context as Troy suggests).

Another good example is the built up genealogy of family bibles inscribed with the names of owners and their family tree which are passed from one generation to the next. To some extent this is highlighted by the passages of the bible in which W begat X begat Y begat Z begat... (Genesis chapters 5 & 11).

These sorts of ancient and modern genealogies also heavily underpinned personal, familial, tribal, and governmental power structures through the ages. Paternity was power.

A well known popular culture version of this appears in the title of the book and film *Harry Potter and the Half-blood Prince* as well as a primary plot point in which Potter actively eschews a beaten up copy of a potions textbook, but to his pleasant surprise find a heavily annotated text that helps him significantly in his studies.

https://harrypotter.fandom.com/wiki/Severus_Snape%27s_copy_of_Advanced_Potion-Making

Yup, see Chapter 6 and our seventh note ;)

I can't help but think of one of the biggest and longest standing puzzles in mathematics in Fermat's Last Theorem. He famously wrote in the margin of a book that he had a proof. but that it was too large to fit in the margin.

https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem

Yes! We really like this example, too, and wondered where to strategically include in the book. Ultimately, we're still searching for the right place or moment to mention Fermat… as you read the book, perhaps you can suggest where it may be best to include this example.

Google has accelerated this by using search to better link pieces of knowledge in the modern world, but scholars have been linking thoughts manually for centuries.

Surprisingly, these have only been recently aggregated online at [Sefaria](https://www.sefaria.org/texts) a story delineated here:

https://www.washingtonpost.com/religion/2018/09/18/quest-put-talmud-online/

Yes, we mention this and link to Sefaria in Chapter 3 when discussing the Talmud and commentary.

Other great examples include teaching and scientific progress. Owen Gingerich details annotations in all the extant copies of Copernicus in his text [The Book Nobody Read: Chasing the Revolutions of Nicolaus Copernicus](https://www.amazon.com/Book-Nobody-Read-Revolutions-Copernicus/dp/B000BNPG8C). There it seemed obvious that the moving state-of-the art of science and teaching was reflected in the annotations made by professors who handed those annotations down to students who also copied them into their textbooks.

Wonderful, we'll include this book in Further Readings .

In some sense this is a textual equivalent of the directors commentary tracks on DVDs from the 1990's in which one could watch films with overdubbed running commentary of the film's director (and often cast, producers, et al. as appropriate).

The first time I recall seeing such journalistic annotations was on the web in The Smoking Gun ( http://www.thesmokinggun.com/ ) which generally annotated court documents that were the source of newsworthy tidbits—generally relating to celebrities or gossip pages.

Great example, and perhaps one we should explicitly mention.

copyediting, will do

remove space

So, let’s say I cringe when writing on the paper and use post-it notes instead, would that not count under your definition? Because it can be removed so isn’t permanent? Pencil annotations can be erased.

This is very much the case for me. And this has also evolved over time, as the purpose of my reading has shifted.

I’m sure that you will also discuss the value of annotation for personal/private use also.

Maybe even the next paragraph. Ha!

It would be fantastic to have the links to these items here in the text. Do you plan to add that later?

I agree with Heather. The link to the NYTimes annotated/searchable version of the Mueller Report would be a good example.

And other texts: the doorjamb with a child’s growth marks, William Burke’s computer log, a building annotated with graffiti, etc.

Loving the stress on agency here. In this context, thinking how notes become a physical manifestation of agency that may occur otherwise without record, like that voice in my head while reading that keeps saying things like “WTF?” or “Exactly!!!!!!).

Maybe there’s a connection here to your earlier concern regarding the “deterministic effects attributed to annotation.” People have agency. People are exercising their agency when adding notes to texts. And when that happens, annotation serves five purposes (as we suggest here in Chapter 1 and explore throughout the book) and it is those annotation purposes that consequently have certain effects… is that helpful?

This moth should become the mascot of multimodal annotation.

?!* couldn’t resist!

Again: maybe “enable” would be a better choice?

Yes, as noted above, we can easily revise throughout.

Just putting a +1 on all of these comments from Nate, Chris, and Troy!

Related to my note above about power in annotation, I feel I need to post a concern here that I’m on the watchout for deterministic effects attributed to annotation as a general technology/practice — rather than to specific social deployments of annotation practices. Each of these outcomes seems like a _possible_, but not _required_ outcome of annotation in specific contexts.

This slightly negative characterization of annotation is a bit jarring as so far we readers have not been presented with a negative view of annotation.

Helpful, thank you.

This sentence made me pause. I certainly think the first clause is worthy, but I’m not sure the the second must always follow. I expect you’ll get into power more later in the book, but based on what I’ve read so far, this seems like a very strong statement to make.

Yes, a strong statement to make. Can we revisit this once you’ve read Chapter 5?

Is the idea that this sentence might link to a way to see possible annotations on this book?

Antero and I have thought a lot about how to create an annotation experience with this manuscript, in both digital and print form, and how that experience can *enable* ongoing conversation. This open review helps to check the box for digital interactions, trails, and spaces. The custom illustrations in every chapter ideally invite reader interaction with the print text (once the book is in hand). And the dedicated hashtag #AnnoConvo will hopefully become another “place” to archive some of that activity. For example, perhaps a future reader annotates one of the custom illustrations - like Fig 5 in this chapter - and then photographs their book/annotation and shares via social media with the #AnnoConvo tag.

For other examples of annotation being used in the sciences, see ClinGen (https://www.clinicalgenome.org/working-groups/biocurators/), NIF (https://neuinfo.org/about/organization), the Qualitative Data Repository (https://qdr.syr.edu/), and SciBot (https://web.hypothes.is/blog/annotation-with-scibot/). I could connect more dots to these or intro folks how know more.

I didn’t know about ClinGen, thank you! QDR is featured in Chapter 2 in our section “Information among Knowledge Communities.” And SciBot is featured in Chapter 7 when we ask, “How should we read human-machine annotation?”

Starting to seem like I only care about em dashes ;) but I think this would read better set apart with em-dashes ;)

Consider replacing throughout with real em-dashes: — ;)

Copyediting - will do ;)

Maybe addressed elsewhere in the text, but it would be nice to see some other examples of fact-checking here. https://climatefeedback.org/ comes to mind, or for a meta-example, Poynter’s “What to expect from fact-checking in 2018”, annotated later to evaluate their predictions (https://www.poynter.org/fact-checking/2017/what-to-expect-from-fact-checking-in-2018/#annotations:16039949). There are likely more…

Thanks for these suggestions, Nate. Climate Feedback is featured in the final section of Chapter 3 (and I hope you appreciate the particular example of peer review that we highlight!). The Poynter resource is great, perhaps that becomes an endnote here?

This sentence is tripping me up with the comma and no “and”. Maybe the comma is more like a colon or em-dash, something like: “Annotation enables journalists to comment more transparently — to share behind-the-scenes or insider perspectives more informally.”?

Yup, a change for clarity. Or maybe also including the word “and” between clauses?

Whenever I see “allows”, I always wonder if “enables” might be a better word choice. Here I think so…

Yes, thank you. We can adjust throughout. That’s a very important distinction related to your broader comments regarding agency.

Love this. In my work, I view “Text” as being very broad. And “notes” on text…or annotation should/could add value to that intersection.

One of the features that I wish I had in a tool like Hypothesis (I guess I have it in Vialogues/VideoANT), but I would love to annotate these different texts, and connect the dots across those spaces. Connect a note on a video to a note on a wikipage to a note on a tweet.

Using annotation to connect across multiple texts and multimedia texts/compositions would make more visible thinking, engagement, and agency of the annotator. Perhaps this is yet to come …

Making me think about affordances of different forms of text (images, hyperlinks, GIFs, video, text) and what that adds or detracts from the text.

This is interesting… if we take an image (or GIF, or other item) that someone else has created, and insert it into our own annotation, have we made it “our own?” Remixed or repurposed it? Can that be considered an annotation, in the sense that we are adding value to the text, or really just a comment of little consequence?

Loving multimodality in here…also the connection to “embers.”

I, too, appreciate that you are already layering in multimodality, even in some print-centric examples of annotation.

In addition to the ideas you are offering about multimodality here, I would also encourage you to look at the seminal work of Gunter Kress, for instance Multimodality: A Social Semiotic Approach to Contemporary Communication .

I’ve viewed these annotation practices (e.g., Hypothesis) as having “discussion about the text baked into the text.” This has the potential to provide a third space for not only dialogue, but growth in a variety of areas.

I’d be curious to know more about the concept of annotation as a “third space” — is the idea that what unfolds in the margins becomes its own, distinct text that can be separated from the original but still stand on its own?

Along with these trends, we do see some that do not value the use/inclusion of annotation on their publishing spaces as they view it as another form of commentary about their work that may modify/limit points made.

This is also making me think about power, access, and digital literacy/savvy. When I first introduce people to Hypothesis, some of their responses are about the fact that “anyone can annotate online” and “on the spaces they already read.” Some view this “invisible layer” of annotation on the Internet as questionable/problematic.

Interesting thought. I’ve always viewed annotation as “additive” or generally positive/beneficial for all. I’ve thought (perhaps it’s my own bias) that redaction is a negative…but now that I’m typing this I realize (I think) that redaction is a type of annotation…and annotation/redaction always benefits someone…it just might not be you. :)

In this respect, you may be very interested to see our discussion about power and redaction in Chapter 5.

!!! I cannot add a GIF here?!?! :)

Exactly…having discussion about the text…baked into the text.

I hope you’re including a mention of Jasper Fforde’s use of the footnoterphone as a tool for conversation across the book world he created in his Thursday Next series.

Might you share a specific link/resource, Bud? Thanks!

But what really, <i>really</i> matters is that the act of making the note is frequently an act of talking back to the text, of recognizing and/or remembering that the author of the text you’re reading isn’t the only person with something to say, and that what they’re saying may not be the last word on the subject.

Bud’s point here about “talking back to the text” is important, and I think that we could employ other prepositions as well.

What does it mean to talk to the text? About the text? Beyond the text? Within the text? Through the text?

Annotation can serve all of these purposes, when approached strategically.

I was gonna say that if I didn’t see a discussion of Marginalia in this book somewhere, I’d be disappointed.

So you lead with a poem ‘bout it. Well played.

If you’re looking for a formal definition of marginalia, as a particular type of annotation, check out Chapter 2 where as also discuss rubrics and rubrication, scholia, and other “forms that inform.”

Librarian approved

Hmm. Me, too, but I didn’t even think about Director Cuts/Commentary DVDs as annotation until you mentioned it

The first annotation I really remember interacting with was all A/V based: VH1 Pop Up Videos and Director Commentaries on the DVD Extras section for movies I liked.

Understanding Annotation: A Comprehensive Guide

What is annotation, the purpose of annotation, types of annotation, how to annotate effectively, annotation tools, annotation examples, annotation in different disciplines, annotation vs. abstract, annotation in digital learning, the future of annotation.

Let's take a journey into the world of annotation, a concept that often makes students cringe and researchers sigh. But, don't worry — this guide will help you understand annotation in a simple, friendly, and clear way. Whether you're a newbie or someone who just needs a refresher, this comprehensive guide will provide a clear definition of annotation and its many uses.

So, what exactly is the definition of annotation? In its simplest form, annotation refers to adding notes or comments to a text or a diagram. It's like having a personal conversation with the author, or making sense of a complex graph. It doesn't stop there, though. The process of annotation is much more than just dropping notes — it's about understanding, interpreting, and engaging with the material. Let's break it down:

• Understanding: Annotations help you to grasp the ideas and concepts presented in the text or diagram. You might underline key phrases or highlight important data points, all in the service of better understanding what you're reading or viewing.
• Interpreting: By providing your own insights or explanations, you're not merely reading or looking at the material, but actively interpreting it. This could be as simple as jotting down "This means..." or "The author is saying..." next to a paragraph.
• Engaging: When you annotate, you're not just a passive reader anymore. You're actively engaging with the material, questioning it, agreeing or disagreeing, even arguing with the author! This active engagement helps to deepen your understanding and retention of the material.

To sum it up, the definition of annotation isn't just about making notes — it's a method to read, understand, interpret, and engage with any piece of content more effectively. And guess what? There's more to annotation than you might think! Stick around as we delve deeper into the purpose, types, and tools of annotation in the following sections.

Now that we've nailed down the definition of annotation, let's talk about why it's so important. Why do teachers, professors, and researchers keep insisting on it? Well, there are several reasons:

• Improves comprehension: Annotating helps you understand the text or diagram better. It's like having a personal guide walking you through a dense forest of words or a complex maze of data. By highlighting and commenting, you can make sense of the material more easily.
• Enhances retention: We've all been there. You read a page, flip it, and — poof! — everything's gone. But with annotation, you can remember more. When you actively engage with the material, you're more likely to remember it. It's like the difference between watching a movie and participating in it.
• Facilitates analysis: Annotation is not just about understanding, but also about analyzing. By adding your own thoughts, insights, and interpretations, you can dig deeper into the material, uncovering layers of meaning that might not be immediately apparent.
• Promotes critical thinking: When you annotate, you're not just accepting information passively — you're actively questioning, evaluating, and critiquing it. This cultivates critical thinking skills, which are crucial in today's information-saturated world.

Remember, the purpose of annotation is not to make your book look like a rainbow or to fill the margins with a clutter of notes. It's about making the material work for you, helping you to understand, remember, analyze, and think critically. So next time someone mentions annotation, don't cringe. Embrace it. It's your secret weapon in the world of learning!

Now that we've got a grip on the definition of annotation and its purpose, it's time to dive into the different types of annotation. You might be thinking, "Wait a minute, there's more than one type?" Yes, indeed! And picking the right one can make a world of difference. So, let's explore:

• Descriptive Annotation: This kind of annotation is like a sneak peek of a movie. It gives an overview of the main points, themes, or arguments without revealing too much. It's like a book cover — enticing enough to draw you in, but not revealing all the secrets.
• Critical Annotation: This type goes a step further. It not only describes the content but also evaluates it. It's like a movie review, discussing the strengths and weaknesses, the relevance of the content, and the author's credibility. It helps you decide whether the material is worth your time.
• Informative Annotation: This annotation is like an all-you-can-eat buffet. It provides a summary of the material, including all the significant findings and conclusions. It's ideal when you need a detailed understanding of the content without having to read the whole thing.
• Reflective Annotation: This type of annotation is a bit more personal. It includes your thoughts, reactions, and reflections on the material. It's like a diary entry, capturing your intellectual journey as you engage with the material.

So, next time you're tasked with annotating, consider the type of annotation that best suits your needs. Remember, the goal is not to make your work harder, but to make it easier and more effective. Happy annotating!

Here you are, equipped with the definition of annotation and an overview of its types. But, how do you do it effectively? Let's break it down:

• Get clear on your purpose: Why are you annotating? Is it to understand better, remember, or critique? Your purpose will guide your annotation process.
• Take a quick preview: Before you start annotating, skim through the material. Get a feel for its structure and main ideas. This way, you'll know what to pay special attention to.
• Be selective: Resist the urge to highlight or underline everything. Limit your annotations to crucial points, unfamiliar concepts, and interesting ideas. The goal is to create signposts that can guide you back to key information when needed.
• Make it meaningful: Don’t just underline or highlight. Write brief notes that summarize, question, or react to the content. This makes your annotations a tool for active learning.
• Use symbols or codes: Develop your own system of symbols or codes to denote different types of information. For example, a question mark could indicate parts you don’t understand, while an exclamation mark could point to surprising or important insights.

Remember, effective annotation is not about how much you mark, but about how well you understand and engage with the material. Keep practicing and refining your approach, and soon you'll become an annotation pro!

So, now that we know how to annotate effectively, let's talk about some tools that can make this process even smoother. These are especially handy if you're dealing with digital content, or if you want to share your annotations with others. Here are some noteworthy ones:

• Pencil and Paper: Sometimes, the old ways are the best ways. Nothing beats the flexibility and simplicity of annotating with a good old-fashioned pencil. You can underline, highlight, make notes in the margin — the possibilities are endless!
• Highlighters: These are great for emphasizing key points in your text. Just remember not to go overboard and turn your page into a rainbow!
• Post-it Notes: If you don't want to write directly on your material, or if you need more space for your thoughts, these little sticky notes can be a lifesaver.
• PDF Annotation Tools: If you're working with digital documents, tools like Adobe Reader, Preview, and others offer built-in annotation features. These can include highlighting, underlining, and adding comments.
• Online Annotation Tools: Websites like Hypothesis and Genius let you annotate web pages and share your annotations with others. They're like social media for readers!

These tools are just the tip of the iceberg. There are many other annotation tools out there, each with its own strengths and weaknesses. So, don't be afraid to experiment and find the ones that work best for you!

Let's put the definition of annotation into real-world scenarios. Here are some examples to help you get a better sense of how annotation works.

• Novels: You're reading a gripping mystery novel and you come across a clue. You underline it and jot down your theories in the margin. That's annotation!
• Textbooks: Remember the last time you studied for an exam? You probably highlighted important information and made notes to help you remember key points. That's annotation too!
• Articles: When reading a long article online, you might use a tool to underline key sections and add your own thoughts. This not only helps you understand the content better but also lets you share your insights with others. Yep, that's annotation.
• Research Papers: If you're conducting research, annotation is your best friend. Underlining important data, writing summaries of complex sections, and noting down your ideas can make the whole process much easier.
• Social Media: Ever added a funny caption to a photo before sharing it with your friends? Guess what? That's annotation too!

As you can see, annotations can be as simple or as complex as you need them to be. They're all about adding extra information to make the original content more useful or meaningful for you. So, next time you're reading something, why not give annotation a try? Who knows, you might discover some fascinating insights!

Now that we've nailed down the definition of annotation, let's see how it's applied across different disciplines. You might be surprised to know that annotation isn't just for the world of literature or academia. Here's how different fields use annotation:

• Sciences: Scientists use annotations to note down observations during experiments. They can also annotate diagrams to explain complex processes.
• Arts: Artists often annotate their sketches with notes about colors, textures, or ideas for future works. Art historians may also use annotations to provide deeper insight into famous paintings or sculptures.
• Computer Science: In the world of coding, annotations can provide extra details about how a piece of code functions. They're like a roadmap for other programmers who might need to understand or modify the code later.
• Geography: Geographers use annotations on maps to highlight specific features or explain certain phenomena. For example, they might annotate a map to show the path of a storm or the spread of a forest fire.
• Business: Business professionals annotate reports and presentations to highlight key points. This helps everyone stay on the same page and understand the main takeaways.

As you can see, no matter the discipline, the power of annotation is universal. It's all about enhancing understanding and fostering communication! So, the next time you're working on a project, why not consider how annotation could help you?

Dealing with academic or professional texts, you've probably come across both annotations and abstracts. But do you know the difference? Many people get confused between the two, but they serve unique roles. Let's clear the air by exploring the definition of annotation versus an abstract:

Annotation: An annotation adds extra information to a text. It could be a comment, explanation, or even a question. Imagine you're reading a complex scientific paper. You might annotate it by jotting down a simpler explanation of a concept in the margins. That's annotation—helping to make the text more accessible and understandable for you.

Abstract: On the other hand, an abstract is a short summary of a document's main points. Think of it as a mini version of the text. If you've ever written a research paper, you've probably had to include an abstract at the beginning. It gives readers a snapshot of what the document covers so they can decide if they want to read the whole thing.

So, in a nutshell, an annotation is more about adding value to the text, while an abstract is about summarizing it. Both have their places and can be super helpful when dealing with complex or lengthy texts. Understanding the difference between the two is another step in mastering the art of reading and writing effectively.

Now, let's shift gears and explore how annotation plays a role in the digital learning space. With the advent of technology, education isn't limited to chalkboards and textbooks anymore. We've moved onto laptops, tablets, and even mobile phones. So, where does the definition of annotation fit in this digital world?

In digital learning, annotation takes on a slightly different form. Instead of scribbling in the margins of a book, you're adding notes to a PDF, highlighting text in an eBook, or leaving comments on a shared document.

Let's say you're studying for a history exam with a friend, and you're both using the same digital textbook. You come across a paragraph that you think is particularly important, so you highlight it and leave a note saying, "Must remember for the exam!" When your friend opens the book on their device, they can see your annotation and benefit from it. This is the power of annotation in digital learning—it promotes collaboration and makes studying a more interactive experience.

And it's not just for students, either. Teachers can use digital annotation to provide feedback on assignments, clarify points in a lecture, or share additional resources. In a world where online learning is becoming the norm, understanding and using digital annotation is a skill worth mastering.

Having explored the definition of annotation in various contexts, it's exciting to imagine where it might head in the future. As we continue to integrate technology into our lives, the role and methods of annotation are likely to evolve with it.

Imagine a world where every bit of text you interact with—be it a digital book, an online article, or even a social media post—can be annotated with your thoughts, questions, or insights. And not just that, imagine those annotations being instantly shareable with anyone around the globe. We're already seeing glimpses of this in digital learning platforms, as we previously discussed.

Moreover, the rise of artificial intelligence might add another layer to annotation. Imagine AI systems that can automatically highlight important parts of a text, suggest resources for further reading, or even generate annotations based on your personal learning style. Now that's a future worth looking forward to!

While we are not there yet, the journey towards that future is already underway. And as we make strides in this direction, the definition of annotation will continue to expand and adapt. It's a fascinating field that underscores the importance of understanding, interpreting, and communicating information in our increasingly interconnected world.

If you're looking to improve your annotation skills and learn more about organizing your creative projects, check out Ansh Mehra's workshop, ' Documentation for Creative People on Notion .' This workshop will provide you with practical tips and techniques for effective annotation, as well as help you develop a comprehensive documentation system for your creative work.

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U.S. Constitution Annotated: Table Of Contents

This edition of the Congressional Research Service's U.S. Constitution Annotated is a hypertext interpretation of the CRS text, updated to the currently published version. It links to Supreme Court opinions, the U.S. Code, and the Code of Federal Regulations, as well as enhancing navigation through search, breadcrumbs, linked footnotes, and tables of contents.

• Article I - The Legislative Branch
• Article II - The Executive Branch
• Article III - The Judicial Branch
• Article IV - The States
• Article V - Amendment
• Article VI - Debts, Supremacy, Oaths
• Article VII - Ratification
• Amendment 1 - Freedom of Religion, Press, Expression
• Amendment 2 - Right to Bear Arms
• Amendment 3 - Quartering of Soldiers
• Amendment 4 - Search and Seizure
• Amendment 5 - Trial and Punishment, Compensation for Takings
• Amendment 6 - Right to Speedy Trial, Confrontation of Witnesses
• Amendment 7 - Trial by Jury in Civil Cases
• Amendment 8 - Cruel and Unusual Punishment
• Amendment 9 - Construction of Constitution
• Amendment 10 - Powers of the States and People
• Amendment 11 - Judicial Limits
• Amendment 12 - Choosing the President, Vice
• Amendment 13 - Slavery Abolished
• Amendment 14 - Citizenship Rights
• Amendment 15 - Race No Bar to Vote
• Amendment 16 - Status of Income Tax Clarified
• Amendment 17 - Senators Elected by Popular Vote
• Amendment 18 - Liquor Abolished
• Amendment 19 - Women's Suffrage
• Amendment 20 - Presidential, Congressional Terms
• Amendment 21 - Amendment 18 Repealed
• Amendment 22 - Presidential Term Limits
• Amendment 23 - Presidential Vote for District of Columbia
• Amendment 24 - Poll Tax Barred
• Amendment 25 - Presidential Disability and Succession
• Amendment 26 - Voting Age Set to 18 Years
• Amendment 27 - Limiting Congressional Pay Increases

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Annotating Texts

What is annotation.

Annotation can be:

• A systematic summary of the text that you create within the document
• A key tool for close reading that helps you uncover patterns, notice important words, and identify main points
• An active learning strategy that improves comprehension and retention of information

Why annotate?

• Isolate and organize important material
• Identify key concepts
• Monitor your learning as you read
• Make exam prep effective and streamlined
• Can be more efficient than creating a separate set of reading notes

How do you annotate?

Summarize key points in your own words.

• Use headers and words in bold to guide you
• Look for main ideas, arguments, and points of evidence
• Notice how the text organizes itself. Chronological order? Idea trees? etc.

Circle Key Concepts and Phrases

• What words would it be helpful to look-up at the end?
• What terms show up in lecture? When are different words used for similar concepts? Why?

Write Brief Comments and Questions in the Margins

• Be as specific or broad as you would like—use these questions to activate your thinking about the content
• See the guide on reading comprehension tips for some examples

Use Abbreviations and Symbols

• Try ? when you have a question or something you need to explore further
• Try ! When something is interesting, a connection, or otherwise worthy of note
• Try * For anything that you might use as an example or evidence when you use this information.
• Ask yourself what other system of symbols would make sense to you.

Highlight/Underline

• Highlight or underline, but mindfully. Check out the resource on strategic highlighting for tips on when and how to highlight.

Use Comment and Highlight Features Built into PDFs, Online/Digital Textbooks, or Other Apps and Browser Add-ons

• Are you using a pdf? Explore its highlight, edit, and comment functions to support your annotations
• Some browsers have add-ons or extensions that allow you to annotate web pages or web-based documents
• Does your digital or online textbook come with an annotation feature?
• Can your digital text be imported into a note-taking tool like OneNote, EverNote, or Google Keep? If so, you might be able to annotate texts in those apps

What are the most important takeaways?

• Annotation is about increasing your engagement with a text
• Increased engagement, where you think about and process the material then expand on your learning, is how you achieve mastery in a subject
• As you annotate a text, ask yourself: "How would I explain this to a friend?"
• Put things in your own words and draw connections to what you know and wonder

The table below demonstrates this process using a geography textbook excerpt (Press 2004):

A common concern about annotating texts: It takes time!

Yes, it can, but that time isn’t lost—it’s invested.

Spending the time to annotate on the front end does two important things:

• It saves you time later when you’re studying. Your annotated notes will help speed up exam prep, because you can review critical concepts quickly and efficiently.
• It increases the likelihood that you will retain the information after the course is completed. This is especially important when you are supplying the building blocks of your mind and future career.

One last tip: Try separating the reading and annotating processes! Quickly read through a section of the text first, then go back and annotate.

Works Consulted

Nist, S., & Holschuh, J. (2000). Active learning: strategies for college success. Boston: Allyn and Bacon. 202-218.

Simpson, M., & Nist, S. (1990). Textbook annotation: An effective and efficient study strategy for college students. Journal of Reading, 34 : 122-129.

Press, F. (2004). Understanding earth (4th ed). New York: W.H. Freeman. 208-210.

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Annotated Bibliographies

What this handout is about.

This handout will explain why annotated bibliographies are useful for researchers, provide an explanation of what constitutes an annotation, describe various types of annotations and styles for writing them, and offer multiple examples of annotated bibliographies in the MLA, APA, and CBE/CSE styles of citation.

Introduction

Welcome to the wonderful world of annotated bibliographies! You’re probably already familiar with the need to provide bibliographies, reference pages, and works cited lists to credit your sources when you do a research paper. An annotated bibliography includes descriptions and explanations of your listed sources beyond the basic citation information you usually provide.

Why do an annotated bibliography?

One of the reasons behind citing sources and compiling a general bibliography is so that you can prove you have done some valid research to back up your argument and claims. Readers can refer to a citation in your bibliography and then go look up the material themselves. When inspired by your text or your argument, interested researchers can access your resources. They may wish to double check a claim or interpretation you’ve made, or they may simply wish to continue researching according to their interests. But think about it: even though a bibliography provides a list of research sources of all types that includes publishing information, how much does that really tell a researcher or reader about the sources themselves?

An annotated bibliography provides specific information about each source you have used. As a researcher, you have become an expert on your topic: you have the ability to explain the content of your sources, assess their usefulness, and share this information with others who may be less familiar with them. Think of your paper as part of a conversation with people interested in the same things you are; the annotated bibliography allows you to tell readers what to check out, what might be worth checking out in some situations, and what might not be worth spending the time on. It’s kind of like providing a list of good movies for your classmates to watch and then going over the list with them, telling them why this movie is better than that one or why one student in your class might like a particular movie better than another student would. You want to give your audience enough information to understand basically what the movies are about and to make an informed decision about where to spend their money based on their interests.

What does an annotated bibliography do?

A good annotated bibliography:

• encourages you to think critically about the content of the works you are using, their place within a field of study, and their relation to your own research and ideas.
• proves you have read and understand your sources.
• establishes your work as a valid source and you as a competent researcher.
• situates your study and topic in a continuing professional conversation.
• provides a way for others to decide whether a source will be helpful to their research if they read it.
• could help interested researchers determine whether they are interested in a topic by providing background information and an idea of the kind of work going on in a field.

What elements might an annotation include?

• Bibliography according to the appropriate citation style (MLA, APA, CBE/CSE, etc.).
• Explanation of main points and/or purpose of the work—basically, its thesis—which shows among other things that you have read and thoroughly understand the source.
• Verification or critique of the authority or qualifications of the author.
• Comments on the worth, effectiveness, and usefulness of the work in terms of both the topic being researched and/or your own research project.
• The point of view or perspective from which the work was written. For instance, you may note whether the author seemed to have particular biases or was trying to reach a particular audience.
• Relevant links to other work done in the area, like related sources, possibly including a comparison with some of those already on your list. You may want to establish connections to other aspects of the same argument or opposing views.

The first four elements above are usually a necessary part of the annotated bibliography. Points 5 and 6 may involve a little more analysis of the source, but you may include them in other kinds of annotations besides evaluative ones. Depending on the type of annotation you use, which this handout will address in the next section, there may be additional kinds of information that you will need to include.

For more extensive research papers (probably ten pages or more), you often see resource materials grouped into sub-headed sections based on content, but this probably will not be necessary for the kinds of assignments you’ll be working on. For longer papers, ask your instructor about their preferences concerning annotated bibliographies.

Did you know that annotations have categories and styles?

Decisions, decisions.

As you go through this handout, you’ll see that, before you start, you’ll need to make several decisions about your annotations: citation format, type of annotation, and writing style for the annotation.

First of all, you’ll need to decide which kind of citation format is appropriate to the paper and its sources, for instance, MLA or APA. This may influence the format of the annotations and bibliography. Typically, bibliographies should be double-spaced and use normal margins (you may want to check with your instructor, since they may have a different style they want you to follow).

MLA (Modern Language Association)

See the UNC Libraries citation tutorial for basic MLA bibliography formatting and rules.

• MLA documentation is generally used for disciplines in the humanities, such as English, languages, film, and cultural studies or other theoretical studies. These annotations are often summary or analytical annotations.
• Title your annotated bibliography “Annotated Bibliography” or “Annotated List of Works Cited.”
• Following MLA format, use a hanging indent for your bibliographic information. This means the first line is not indented and all the other lines are indented four spaces (you may ask your instructor if it’s okay to tab over instead of using four spaces).
• Begin your annotation immediately after the bibliographic information of the source ends; don’t skip a line down unless you have been told to do so by your instructor.

APA (American Psychological Association)

See the UNC Libraries citation tutorial for basic APA bibliography formatting and rules.

• Natural and social sciences, such as psychology, nursing, sociology, and social work, use APA documentation. It is also used in economics, business, and criminology. These annotations are often succinct summaries.
• Annotated bibliographies for APA format do not require a special title. Use the usual “References” designation.
• Like MLA, APA uses a hanging indent: the first line is set flush with the left margin, and all other lines are indented four spaces (you may ask your instructor if it’s okay to tab over instead of using four spaces).
• After the bibliographic citation, drop down to the next line to begin the annotation, but don’t skip an extra line.
• The entire annotation is indented an additional two spaces, so that means each of its lines will be six spaces from the margin (if your instructor has said that it’s okay to tab over instead of using the four spaces rule, indent the annotation two more spaces in from that point).

CBE (Council of Biology Editors)/CSE (Council of Science Editors)

See the UNC Libraries citation tutorial for basic CBE/CSE bibliography formatting and rules.

• CBE/CSE documentation is used by the plant sciences, zoology, microbiology, and many of the medical sciences.
• Annotated bibliographies for CBE/CSE format do not require a special title. Use the usual “References,” “Cited References,” or “Literature Cited,” and set it flush with the left margin.
• Bibliographies for CSE in general are in a slightly smaller font than the rest of the paper.
• When using the name-year system, as in MLA and APA, the first line of each entry is set flush with the left margin, and all subsequent lines, including the annotation, are indented three or four spaces.
• When using the citation-sequence method, each entry begins two spaces after the number, and every line, including the annotation, will be indented to match the beginning of the entry, or may be slightly further indented, as in the case of journals.
• After the bibliographic citation, drop down to the next line to begin the annotation, but don’t skip an extra line. The entire annotation follows the indentation of the bibliographic entry, whether it’s N-Y or C-S format.
• Annotations in CBE/CSE are generally a smaller font size than the rest of the bibliographic information.

After choosing a documentation format, you’ll choose from a variety of annotation categories presented in the following section. Each type of annotation highlights a particular approach to presenting a source to a reader. For instance, an annotation could provide a summary of the source only, or it could also provide some additional evaluation of that material.

In addition to making choices related to the content of the annotation, you’ll also need to choose a style of writing—for instance, telescopic versus paragraph form. Your writing style isn’t dictated by the content of your annotation. Writing style simply refers to the way you’ve chosen to convey written information. A discussion of writing style follows the section on annotation types.

Types of annotations

As you now know, one annotation does not fit all purposes! There are different kinds of annotations, depending on what might be most important for your reader to learn about a source. Your assignments will usually make it clear which citation format you need to use, but they may not always specify which type of annotation to employ. In that case, you’ll either need to pick your instructor’s brain a little to see what they want or use clue words from the assignment itself to make a decision. For instance, the assignment may tell you that your annotative bibliography should give evidence proving an analytical understanding of the sources you’ve used. The word analytical clues you in to the idea that you must evaluate the sources you’re working with and provide some kind of critique.

Summary annotations

There are two kinds of summarizing annotations, informative and indicative.

Summarizing annotations in general have a couple of defining features:

• They sum up the content of the source, as a book report might.
• They give an overview of the arguments and proofs/evidence addressed in the work and note the resulting conclusion.
• They do not judge the work they are discussing. Leave that to the critical/evaluative annotations.
• When appropriate, they describe the author’s methodology or approach to material. For instance, you might mention if the source is an ethnography or if the author employs a particular kind of theory.

Informative annotation

Informative annotations sometimes read like straight summaries of the source material, but they often spend a little more time summarizing relevant information about the author or the work itself.

Indicative annotation

Indicative annotation is the second type of summary annotation, but it does not attempt to include actual information from the argument itself. Instead, it gives general information about what kinds of questions or issues are addressed by the work. This sometimes includes the use of chapter titles.

Critical/evaluative

Evaluative annotations don’t just summarize. In addition to tackling the points addressed in summary annotations, evaluative annotations:

• evaluate the source or author critically (biases, lack of evidence, objective, etc.).
• show how the work may or may not be useful for a particular field of study or audience.
• explain how researching this material assisted your own project.

Combination

An annotated bibliography may combine elements of all the types. In fact, most of them fall into this category: a little summarizing and describing, a little evaluation.

Writing style

Ok, next! So what does it mean to use different writing styles as opposed to different kinds of content? Content is what belongs in the annotation, and style is the way you write it up. First, choose which content type you need to compose, and then choose the style you’re going to use to write it

This kind of annotated bibliography is a study in succinctness. It uses a minimalist treatment of both information and sentence structure, without sacrificing clarity. Warning: this kind of writing can be harder than you might think.

Don’t skimp on this kind of annotated bibliography. If your instructor has asked for paragraph form, it likely means that you’ll need to include several elements in the annotation, or that they expect a more in-depth description or evaluation, for instance. Make sure to provide a full paragraph of discussion for each work.

As you can see now, bibliographies and annotations are really a series of organized steps. They require meticulous attention, but in the end, you’ve got an entire testimony to all the research and work you’ve done. At the end of this handout you’ll find examples of informative, indicative, evaluative, combination, telescopic, and paragraph annotated bibliography entries in MLA, APA, and CBE formats. Use these examples as your guide to creating an annotated bibliography that makes you look like the expert you are!

MLA Example

APA Example

CBE Example

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

American Psychological Association. 2010. Publication Manual of the American Psychological Association . 6th ed. Washington, DC: American Psychological Association.

Bell, I. F., and J. Gallup. 1971. A Reference Guide to English, American, and Canadian Literature . Vancouver: University of British Columbia Press.

Bizzell, Patricia, and Bruce Herzburg. 1991. Bedford Bibliography for Teachers of Writing , 3rd ed. Boston: Bedford Books.

Center for Information on Language Teaching, and The English Teaching Information Center of the British Council. 1968. Language-Teaching Bibliography . Cambridge: Cambridge University.

Engle, Michael, Amy Blumenthal, and Tony Cosgrave. 2012. “How to Prepare an Annotated Bibliography.” Olin & Uris Libraries. Cornell University. Last updated September 25, 2012. https://olinuris.library.cornell.edu/content/how-prepare-annotated-bibliography.

Gibaldi, Joseph. 2009. MLA Handbook for Writers of Research Papers , 7th ed. New York: The Modern Language Association of America.

Huth, Edward. 1994. Scientific Style and Format: The CBE Manual for Authors, Editors, and Publishers . New York: University of Cambridge.

Kilborn, Judith. 2004. “MLA Documentation.” LEO: Literacy Education Online. Last updated March 16, 2004. https://leo.stcloudstate.edu/research/mla.html.

Spatt, Brenda. 1991. Writing from Sources , 3rd ed. New York: St. Martin’s.

University of Kansas. 2018. “Bibliographies.” KU Writing Center. Last updated April 2018. http://writing.ku.edu/bibliographies .

University of Wisconsin-Madison. 2019. “Annotated Bibliography.” The Writing Center. Accessed June 14, 2019. https://writing.wisc.edu/handbook/assignments/annotatedbibliography/ .

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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• Open access
• Published: 17 May 2024

Identification of patients’ smoking status using an explainable AI approach: a Danish electronic health records case study

• Ali Ebrahimi 1 ,
• Margrethe Bang Høstgaard Henriksen 2   na1 ,
• Claus Lohman Brasen 3 , 4 ,
• Ole Hilberg 5 , 4 ,
• Torben Frøstrup Hansen 2 , 4 ,
• Lars Henrik Jensen 2 ,
• Abdolrahman Peimankar 1 &
• Uffe Kock Wiil 1  

BMC Medical Research Methodology volume  24 , Article number:  114 ( 2024 ) Cite this article

Metrics details

Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs). This would require researchers and clinicians to manually navigate through extensive amounts of unstructured data, which is one of the main reasons that smoking habits are rarely integrated into larger studies. Our aim is to develop machine learning models to classify patients’ smoking status from their EHRs.

This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients’ smoking status and providing explanations for the decisions. The proposed NLP pipeline comprises four distinct components, which are; (1) considering preprocessing techniques to address abbreviations, punctuation, and other textual irregularities, (2) four cutting-edge feature extraction techniques, i.e. Embedding, BERT, Word2Vec, and Count Vectorizer, employed to extract the optimal features, (3) utilization of a Stacking-based Ensemble (SE) model and a Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) for the identification of smoking status, and (4) application of a local interpretable model-agnostic explanation to explain the decisions rendered by the detection models. The EHRs of 23,132 patients with suspected lung cancer were collected from the Region of Southern Denmark during the period 1/1/2009-31/12/2018. A medical professional annotated the data into ‘Smoker’ and ‘Non-Smoker’ with further classifications as ‘Active-Smoker’, ‘Former-Smoker’, and ‘Never-Smoker’. Subsequently, the annotated dataset was used for the development of binary and multiclass classification models. An extensive comparison was conducted of the detection performance across various model architectures.

The results of experimental validation confirm the consistency among the models. However, for binary classification, BERT method with CNN-LSTM architecture outperformed other models by achieving precision, recall, and F1-scores between 97% and 99% for both Never-Smokers and Active-Smokers. In multiclass classification, the Embedding technique with CNN-LSTM architecture yielded the most favorable results in class-specific evaluations, with equal performance measures of 97% for Never-Smoker and measures in the range of 86 to 89% for Active-Smoker and 91–92% for Never-Smoker.

Our proposed NLP pipeline achieved a high level of classification performance. In addition, we presented the explanation of the decision made by the best performing detection model. Future work will expand the model’s capabilities to analyze longer notes and a broader range of categories to maximize its utility in further research and screening applications.

Peer Review reports

Introduction

Information on smoking status is crucial especially in cardiovascular, pulmonary, diabetes, and cancer research, since in addition to being a common risk factor it is also a confounder for various diseases [ 1 ]. Smoking accounts for more than eight million deaths annually [ 2 ]. In the specific area of lung cancer, the implementation of screening and detective models is becoming more relevant. The models, however, lack the ability to identify high-risk individuals who are dependent on tobacco [ 3 ]. In Denmark, smoking habits are not formally registered unless patients are diagnosed with cancer or a chronic disease that includes them in the National Clinical Registries. For example, information on the smoking habits of a lung cancer patient will be registered in the Danish Lung Cancer Registry, while a patient with chronic obstructive pulmonary disease, followed at a hospital level, will appear in the Danish Register of Chronic Obstructive Lung Disease [ 4 ]. Patients with milder conditions often do not appear in national registries, and information on smoking habits is only available as unstructured free-text in electronic health records (EHRs) [ 5 ]. The records often have an unrestricted format leading to differences between clinicians in terms of spelling errors, abbreviations, and a field-specific jargon that may be difficult for outsiders to interpret [ 6 ]. Clinicians have to manually search for smoking habits, which is feasible when dealing with a small number of patients, but it becomes impractical with larger cohorts, such as a high-risk population for lung cancer screening or large-scale research with smoking as an essential risk factor [ 7 ].

Natural Language Processing (NLP), a sub-field of artificial intelligence, focuses on analyzing linguistic data, particularly unstructured textual data using machine learning. The main goal of NLP is to transform free text into structured data that can be easily identified by machines [ 8 ]. NLP has been used in healthcare for various tasks such as detecting heart failure criteria [ 9 ], identifying adverse drug effects [ 10 ] detecting symptoms of specific disease, and improving quality of life [ 11 ]. In 2006, the “Informatics for Integrating Biology and the Bedside” research center announced the “Smoking challenge” funded by the National Institute of Health in the USA. The challenge aimed to address the problem of classifying smoking status based on EHRs and compare the performance with classifications made by pulmonologists. By means of supervised and unsupervised classifiers, several models demonstrated the ability to classify smoking status using a limited number of key textual features [ 12 ]. More recently, applying deep neural networks to EHRs has been in focus due to their better performance and lower preprocessing requirements [ 13 , 14 ]. In 2018, Google developed a new technique called Bidirectional Encoder Representations from Transformers (BERT). Unlike traditional word embedding methods such as word2vec, BERT is context-sensitive and generates a representation of each word based on the other words in the sentence [ 15 , 16 ]. BERT is considered state-of-the-art, as it allows for transfer learning and adaptations to other domains [ 17 ]. In 2020, a Danish edition of BERT was introduced, trained on 1.6 billion words from various online repositories (Common Crawl, Wikipedia, OpenSubtitles, etc.) [ 18 , 19 ]. Additionally, in 2021, Derczynski et al. presented the first Danish Gigaword Corpus, a billion-word corpus encompassing a wide range of the Danish language from different domains, settings, time periods, registers, and dialects [ 20 ].

Despite the advancements, a high-performing model capable of detection smoking status in the Danish language is yet to be developed. This limitation can be attributed to both the limited availability of text data due to access restrictions and the lack of advanced model development. The complex structure of EHRs further limits the possibility of transfer learning from other languages [ 21 ]. Consequently, this paper aims to address these challenges by presenting a high-performing NLP-based model capable of detecting smoking status in Danish EHRs using both binary and multiclass labels. The model is expected to be valuable in future screening scenarios and various research fields, including other types of cancer and cardiovascular diseases.

As machine learning and deep neural networks continue to advance, they often remain mysterious for both developers and end-users, resembling black boxes. The lack of transparency obstructs the broad adoption of such models, especially in domains where decision making holds critical importance such as the medical field. To effectively implement a model in a medical context, explainability becomes imperative in allowing clinicians and researchers to trust and comprehend the precise detection made [ 22 ]. An explainable model enhances the chance of identifying systematic errors and hence improves the model’s performance. Understanding the rationale behind a detection and the potential for model enhancement is of utmost importance for clinicians or researchers who will ultimately be responsible for the outcomes. While research and applications in explainable artificial intelligence have grown in the context of image and structured data models, those based on free-text datasets have received comparatively less attention [ 23 ]. Consequently, in addition to developing highly accurate detection models, this study seeks to provide transparent post-hoc explanations for the models.

The contributions of our study can be summarized as follows:

Formulate several NLP-based architectures to identify smoking status : To the best of our knowledge, this is the first study to detect smoking status based on Danish text from EHR. Several NLP-based architectures formulated resulting from the integration of advanced feature extraction techniques with ensemble-based machine learning and deep learning models.

Analyzing the detection performance of developed architectures and comparing them with state-of-the-art detection models : Comprehensive analysis and comparison of the performance of the developed models against existing state-of-the-art predictive models, with the superior models identified through rigorous statistical evaluation. This not only highlighted the detection performance of our model in comparison to others but also explored into a non-parametric statistical assessment based on the Friedman test.

Post-hoc explanations for the detection models : The study is the first study to provide model explanations for smoking status detection based on EHR. Explanation of the models’ decision-making processes using the state-of-the-art XAI approach, LIME, highlighting the significance of individual features and the underlying rationale for model decisions.

The subsequent sections outline the process of data collection followed by preprocessing, feature extraction, model development, evaluation, and explanation. Figure  1 provides a comprehensive overview of the study’s methodology, encompassing all stages of the pipeline.

Data collection

Data for this project were obtained from EHRs within a cohort of 38,944 patients who underwent assessments for a potential risk of lung cancer between January 1, 2009 and December 31, 2018 in the Region of Southern Denmark. This cohort has been comprehensively described in a related work [ 24 ]. We collected all types of documents from the EHRs containing the subheaders “smoking” or “risk factors” without imposing any time constraints. The subheaders were chosen, as they most commonly contain documentation of smoking history. Moreover, the data annotation process would have been impractical which we used on complete patient notes from the EHRs. We carefully eliminated duplicate entries, instances with missing gender information, and we pseudonymized the data to ensure privacy and confidentiality.

Pre-processing

Clinical notes underwent manual annotation by a medical doctor and the results were subsequently reviewed by the same doctor. The dataset underwent further refinement with a decision to include only one note per patient. As the annotated data have been employed in previous studies to predict lung cancer status, our selection focused on the note that provided the most comprehensive details regarding smoking status. Patients were primarily categorized as Active Smoker if they had detailed information on current pack-years (a widely recognized measure of smoking intensity calculated by multiplying the number of packs of cigarettes smoked per day by the number of years of smoking) [ 25 ]. The remaining patients, lacking information on pack-year, were categorized as Active-Smoker or Former-Smoker, or status unknown. To resolve the “unknown” category, additional notes for these patients were evaluated and a smoking label was assigned based on the note containing the most comprehensive information. Any duplicate entries were removed, retaining only the note responsible for the patient’s label.

To validate the smoking status annotation from the EHRs, the distributions were compared with registrations of pack-years obtained from the Danish Lung Cancer Registry. They had been recorded independently of the EHRs and manually completed by clinicians upon a patient’s lung cancer diagnosis. While the registration of smoking status from the Danish Lung Cancer Registry is not integrated in the EHRs, it is expected to align with the EHRs annotations overall. Following the annotation process the text underwent cleaning in the following sequence: Handling of abbreviations, conversion of all text to lowercase, removal of stop words, numbers, and punctuation marks. Consecutive spaces where there were two or more spaces in a row were either removed or converted to a single space. Finally, a word tokenizer was applied to convert sentences into word tokens.

The cleaning steps employed in this study are carefully tailored to enhance the analysis of Danish EHRs, recognizing the unique linguistic and structural characteristics of the language. Handling abbreviations initially is crucial in Danish language, where abbreviations can carry significant meanings or denote specific terminology, ensuring that such condensed forms are correctly interpreted or expanded for analysis. Converting all text to lowercase addresses the case sensitivity of Danish language, promoting uniformity and reducing the risk of duplicate representations for the same words.

The removal of stop words, numbers, and punctuation marks, beside the consolidation of consecutive spaces, streamlines the text, focusing the analysis on the most meaningful content without the noise of non-informative elements. This step is particularly effective in Danish, where functional words and punctuation can obscure key linguistic patterns if not properly managed. Applying a word tokenizer as the final step effectively breaks down sentences into individual tokens, a process that is essential for capturing the morphological richness of Danish words and phrases. Each of these steps, collectively, prepares the Danish EHRs for a more accurate and efficient computational analysis, ensuring that subsequent NLP tasks, such as feature extraction and model training, are performed on clean, consistent data that accurately reflects the intricacies of the Danish language.

Given the challenge of imbalanced class distribution, a stratified split approach was chosen, which entailed dividing the data into a training set (70%) and a test set (30%). By using a stratified split, the proportion of records in all classes remained consistent between the training and test sets. Preprocessing techniques, including data cleaning and feature extraction, were exclusively learned from the training set, and subsequently applied to the test set with necessary adaptations. This prevented a possible information leakage from the test set to the model training process, which could have led to an overly optimistic evaluation of model performance. It is important to note that the test set was exclusively used for evaluating the performance of the final models and did not contribute to the model learning process.

Feature extraction

Before choosing a classification algorithm for the task, it is essential to transform the unstructured data into a numerically vectorized representation. Feature extraction can be done with word embedding methods referring to the representation of words and whole sentences in a numerical manner. Words are converted into numeric vectors, and vectors of words closely related would be closer to each other [ 26 ]. In this study, we consider three methods to encode the tokens of a given technical text into a vectorized representation: The well-known Word embedding, BERT, Count Vectorizer and Word2Vector. General descriptions of all methods are described in detail in Table  1 . We applied a hyperparameter tuning step for the Count Vectorizer method using a randomized search cross validation to identify the threshold for the removal of frequent tokens and the number of n-grams.

Selecting Word Embedding, BERT, Count Vectorizer, and Word2Vec as methods for encoding tokens of Danish EHR into vector representations aligns with our objective to capture the linguistic nuances inherent to the Danish language effectively. Word Embeddings and Word2Vec, both deeply rooted in learning contextual relationships and semantic similarities, are particularly adept at navigating the intricate morphological characteristics of Danish language, such as its compound words and diverse verb forms. These methods excel in creating nuanced vector representations that reflect the semantic richness of words within their specific context, a crucial feature for the Danish language with its nuanced meanings and expressions.

BERT, with its deep contextualized training, excels in understanding the syntax and semantics of Danish text, leveraging its transformer architecture to capture subtle language cues and idiomatic expressions unique to Danish language. This is particularly beneficial given the contextual richness and syntactic flexibility of Danish. Lastly, Count Vectorizer provides a straightforward yet powerful approach to text representation, capturing the frequency of terms in a manner that supports the identification of domain-specific terminology prevalent in technical texts. Additionally, these methods provide a comprehensive toolkit for Danish text analysis, balancing deep semantic understanding with robust statistical approaches to ensure accurate and meaningful representation of Danish EHR.

Model development

Stacking-based ensemble (se).

The SE method was created by Wolpert et al. and is different from previous ensemble learning techniques in that it employs meta-learning to combine multiple types of machine learning algorithms [ 30 ]. SE is used in a two-level structure where the level-1 meta learner combines the outputs of the level-0 base learners. Figure  1 , Sect. 4 illustrates the stacking structure used in this study, which comprises three stages. The first stage involves training the base classifiers, which are K-Nearest Neighbor, Decision Trees, Random Forest, and XGBoost algorithms. The second stage involves gathering the output detection (feature vectors) of the base classifiers to generate a new reorganized training set. Finally, in the third stage, the Logistic Regression algorithm is utilized to train the meta-classifier using the new training set, resulting in the development of SE. Detailed descriptions of the developed machine learning algorithms are provided in Table  2 .

For the detection of smoking status, we also used the architecture CNN-LSTM. It consists of five layers, i.e., an input layer for word embedding, a one-dimensional convolutional network layer for local feature extraction, an LSTM network layer for capturing long-term dependencies, a dropout layer, and a classification layer for label detection. The structure of our model is shown in Fig.  1 (Sect. 4). In the input layer, input texts are treated as a matrix. Each row of the matrix represents a word, derived from the feature extraction method. In this study, the dimension of 300 is considered for the input layer. We used a one-dimensional convolution layer (Conv1D) to capture the sequence information and reduce the dimensions of the input data. A convolution operation involves a convolutional kernel applied to a fixed window of words to compute a new feature. The kernel, also called a filter, completes the feature extraction. Each filter is applied to a window of m words to obtain a single feature. To ensure the integrity of the word as the smallest granularity, the width of the filter is equal to the width of the original matrix. In this study, we employed the Conv1D layer with 256 filters and a kernel size of 3 in the output of the embedding layer to learn the lower-level features from words. A nonlinear activation function ReLU is used to reduce the number of iterations needed for convergence in deep networks.

Following the above steps, the result of the convolution was pooled using the maximum pooling operation to capture essential features in the text. To improve the quality of our text classification task, the different calculated features were concatenated to constitute the input of the LSTM layer. LSTM solves the vanishing gradient problem because it learns to regulate the flow of information. Due to high memory power, LSTMs can efficiently capture contextual information from the input text and produce high-level features that are used for further classification. We added a dropout layer to reduce the chance of overfitting. Finally, the last component is the fully connected layer, which takes as input the characteristics generated from a sentence by the LSTM layer and consequently detects the most appropriate label according to semantic and syntactic content. The probability that a sentence belongs to the smoking categories is calculated by the Softmax activation function.

Model architectures

Combining the different feature extraction methods with CNN-LSTM and the SE resulted in seven architectures: (1) Embedding with CNN-LSTM, (2) Embedding with SE, (3) Bert with CNN-LSTM, (4) Bert with SE, (5) Word2Vector with CNN-LSTM, (6) Word2Vector with SE, and (7) Count Vectorizer with SE. The details of these architectures are presented in Fig.  1 , Sect. 4.

In this study, we chose not to employ the combination of Count Vectorizer with a CNN-LSTM architecture. The rationale behind the decision lies in the intrinsic design of the Count Vectorizer, which produces a bag-of-words representation, consequently discarding word order. CNN-LSTM architectures are specifically tailored to capture sequential patterns in data; therefore, using a bag-of-words representation compromises their primary advantage. Furthermore, the integration of CNN-LSTM introduces substantial complexity to the model. In the absence of sequential data to leverage the unique strengths of CNN-LSTM, alternative simpler models may potentially offer comparable or superior performance without the computational overhead of such intricate architectures.

Model evaluation

To assess the detection performance of the created classifiers, several metrics were employed, including the receiver operating characteristics curve (ROC), area under the receiver operating characteristics curve (AU-ROC), Precision, Recall, F1-Score, and detection accuracy. These performance metrics are determined by searching for the values of true positive (TP), false positive (FP), false negative (FN), and true negative (TN). Detailed descriptions of the evaluation metrics used are presented in Table  3 .

Model explanation

Explainable Artificial Intelligence (XAI) techniques helps to explain the decisions made by machine learning models so that humans can understand. Ensuring that clinical staff and end users trust a machine learning model’s decisions requires making it’s reasoning process clear and comprehensible [ 37 ]. The local interpretable model-agnostic explanations (LIME) framework is one of the most extensively used XAI packages that enables classifiers to explain individual detection [ 38 ]. It explains a decision by locally approximating the classifier’s decision boundary in the given instance’s neighborhood. LIME builds locally linear models to explain the detection of a machine learning model. It corresponds to the rule-based regional explanations through the simplification category. Explanations through simplification build an entirely new model based on the trained machine learning model to be explained. The newly simplified model then attempts to optimize its similarity to its previous model functions while lowering complexity and maintaining comparable performance. As a result, after the machine learning decision is achieved, the LIME is used to assess the features’ importance and probabilities in the decision. As a result, we can determine the importance of the features in the decision input, which assists in interpreting the model outputs. We applied this technique to the models, which has the highest detection. Since the data are private and contain sensitive information, only the non-sensitive portions of the sentences are displayed in the examples.

Dataset description

From the total cohort of patients examined on suspicion of lung cancer ( N  = 38,944), notes containing the two subheaders were available on 23,542 patients (59%). After removing duplicates and patients missing data on gender, the final cohort was reduced to 23,132 patients, each with multiple registrations (92,113 notes). Each note contained an average of 60 tokens, but the range of the token length varied between 1 and 1051. The annotation of the 23,132 patients with available notes resulted in the following distribution of smoking habits: 6121 (26%) Never-Smoker, 10,617 (46%) Former-Smoker and 6394 (28%) Active-Smoker. They were further pooled into binary labels of Non-Smoker (26%) and Smoker (74%), which is former and active smokers.

To validate the data annotation, the results were matched against the registrations in the Danish Lung Cancer Registry. From the 23,132 patients with EHR-annotated smoking status, 4719 had lung cancer. Among these, data on smoking status registered in the Danish Lung Cancer Registry was available on 4168 patients. In the registry 217 patients were listed as Non-Smoker, of which the EHR annotation was equivalent in 83% of the cases. The registration as Smoker was made on 3787 patients of which the EHR annotation was equivalent in 97% of cases. This was overall considered to be a high correlation between the results and acceptable validity of the manual annotation from free text.

Binary classification

It is important to note that in terms of precision, recall, and F1-score, the SE-based architecture was low on average and class-specific performance. As presented in Fig.  2 , BERT with SE and Embedding with SE achieved the worst results compared with the other feature extraction methods, in which the accuracy reached 97%. This might be due to high dimensionality, causing the SE to be less effective when compared to alternative methods. On the other hand, BERT with CNN-LSTM could achieve almost the highest overall accuracy and precision of 99% among all developed architecture. However, as shown in Table  4 , BERT using CNN-LSTM shared the best precision of 99% with Embedding using the CNN-LSMT architecture for the Smoker class.

In terms of recall, Embedding with CNN-LSTM and Count Vectorizer with SE achieved the highest precision of 98% as shown in Fig.  2 . For the single class of Smoker, however, Bert with CNN-LSTM achieved the highest recall of 100% (Table  4 ). In terms of F1-Score, Word2Vector achieved the highest overall performance of 98%. As to F1-Score of a single class of Smoker, three architectures achieved the highest score of 99%, i.e., BERT with CNN-LSTM, Word2Vector with CNN-LSTM and Count Vectorizer with SE.

Results based on confusion matrix (Fig.  3 ) indicates that Word2Vector with CNN-LSTM architecture had the best performance in terms of detecting Smoker class with a true detection rate of about 99%. BERT with CNN-LSTM architecture performed best in detecting Non-Smoker patients at a true detection rate of about 98%. The results of other machine learning classifiers including KNN, DT, RF, and XGBoost are presented in Supplementary Fig.  1 and Supplementary Table 1 .

Multiclass classification

As presented in Fig.  4 , BERT with SE had the lowest performance compared to the other feature extraction methods, in which the accuracy reached 89%. Contrarily, BERT with CNN-LSTM achieved the highest accuracy, precision, recall, F1-score, and AUC of 95%. This architecture also performed the best in most of the class specific outcomes. As presented in Table  5 , BERT with CNN-LSTM had the highest performance for precision and F1-score of the Never-Smoker and Active-Smoker classes. In terms of precision, this architecture achieved 98% and 95% in the Never-Smoker and Active-Smoker classes, respectively. In terms of F1-score, it achieved 97% and 93% in the Never-Smoker and Active-Smoker classes, respectively.

Other architectures also achieved reasonable detection performances close to the performance of BERT with CNN-LSTM architecture. Embedding with CCN-LSTM and Count Vectorizer with SE achieved an overall accuracy of 94% (Fig.  4 ), which is only 1% lower than BERT with CNN-LSTM. Considering the results in Table  5 , Embedding with CCN-LSTM and BERT with CNN-LSTM architecture achieved the highest precision and F1-scores of 94% and 95%, respectively, for the Former-Smoker class. In terms of recall, the results for each class varied. For the Never-Smoker class, Count Vectorizer with SE achieved the highest recall of 98%. For the Active-Smoker class, Embedding with CNN-LSTM, BERT with CNN-LSTM, and Count Vectorizer with SE achieved the highest recall of 91%. In the Former-Smoker class, BERT with CNN-LSTM achieved the highest recall of 97%.

Results derived from the confusion matrix reveal that the Embedding with CNN-LSTM and Count Vectorizer with SE architectures exhibited superior performance in detecting the Active-Smokers and Never-Smoker classes, yielding true detection rates of approximately 91% and 97%, respectively (Fig.  5 ). BERT with CNN-LSTM excelled in identifying samples from the Former-Smoker class, with a true detection rate of 98%. When accounting for the smallest discrepancy in detection rates across all classes, both the Embedding with CNN-LSTM and Count Vectorizer with SE architectures were the most consistent. This suggests a marginal difference of about 4% between the Former-Smoker and Active-Smoker classes, which is the narrowest gap observed across all architectures. The marginal difference between the Never-Smoker class and other classes in the Embedding with CNN-LSTM architecture presents the narrowest gap compared to all other architectures developed. The results of other machine learning classifier including KNN, DT, RF, and XGBoost are presented in Supplementary Fig.  2 and Supplementary Table 2 .

Post-hoc comparison of model architectures

Since the results derived from detection performances and confusion matrices did not provide sufficient insight to determine the optimal model, we conducted a Friedman test on the mean of average results from the seven developed architectures. As shown in Fig.  6 there was no significant difference in average performance between the classifiers, neither concerning the binary (A) nor the multiclass architectures (B).

XAI to explain detection model decisions

The results indicate that classifying between ‘Former-Smoker’ and ‘Active-Smoker’ status is challenging, as the models occasionally underperformed in these categories. Nonetheless, the architecture of Embedding with CNN-LSTM reached a nearly optimal performance. In this section we explain the framework of the architecture utilizing the LIME technique as depicted in Fig.  6 . All examples come with the original text and plots illustrating the importance of features for the detected class compared to the remaining two classes. Figure  7 A displays the data on a Former-Smoker accurately detected with a probability of 94% of being categorized as a Former-Smoker. The key feature, “rygeophør” (smoking cessation), played a central role in assigning the case to the Former-Smoker category. Figure  7 B presents the data of an Active-Smoker that was correctly detected with a probability of 100% as an Active-Smoker. This outcome was primarily influenced by the words “fortsat” (continued) and “dgl” (daily), which classified the patient into the Active-Smoker category. Figure  7 C, however, portrays an Active-Smoker that was misclassified as a Former-Smoker, with a detected high probability of 99% of being a Former-Smoker and merely 1% of being an Active-Smoker. The words “rygestop” (smoking cessation) and “2017” contributed significantly to the detection, while the words “dagligt” (daily) and “ryger” (smoker) skewed the classification toward the Active-Smoker label. Figure  7 D exhibits a Smoker incorrectly labeled as a Non-Smoker, due to the misinterpretation of the word “nihil” (nothing) within an alcohol assessment context.

Summary of findings.

We proposed effective detection NLP-based architectures for detection of smoking status using Danish EHRs. The data were collected from 23,132 patients who underwent examinations on suspicion of lung cancer. They were conducted at pulmonary departments in the Region of Southern Denmark from 2009 to 2018. Our proposed method encompassed the utilization of seven diverse model architectures developed through a combination of feature extraction techniques (embedding, BERT, Word2Vector, and count vectorizer), machine learning (SE) and deep learning (CNN-LSTM) models. We evaluated the performance of the architectures by examining various metrics for binary (Non-Smoker and Smoker) and multiclass (Never-Smoker, Active-Smoker, and Former-Smoker) classification tasks. Each metric focuses on a special aspect of the performance. Except for the AU-ROC, all metrics were constructed based on a confusion matrix (TP, FP, TN, and FN).

Given the complex nature of Danish language, particularly its compound word formation and unique syntactic structures, our proposed methodology was accurately designed to ensure the relevance and effectiveness of selected NLP pipeline in processing Danish language. The developed models and feature extractions were chosen for their robust linguistic capture capabilities, essential for the syntactic and morphological complexities of Danish. Adaptations included specialized preprocessing for Danish abbreviations and punctuation, and the fine-tuning of the BERT model with Danish EHR, enhancing its syntactic and semantic understanding of the language. The superior performance of the developed scenarios within our experimental validation highlights the success of these adaptations. Such outcomes not only validate our methodological choices but also underline the potential of our approach in advancing Danish language processing.

Performance metrics exhibited general similarity across the models, and post hoc tests revealed no significant differences when considering the mean of all outcomes. In terms of binary classification, however, the evaluations specific to each class indicated that BERT with CNN-LSTM outperformed the other models in all performance metrics.

In terms of multiclass classification, we observed that BERT with SE achieved the worst results compared with the other feature extraction methods in which the accuracy reached 89%. This was somehow expected due to the low amount of labeled data. BERT embeddings are high-dimensional vectors, which can lead to a large number of features when applied to the classical machine learning models. It resulted in high dimensionality causing the SE to become less efficient compared to other techniques.

On the other hand, the architecture of BERT with CNN-LSTM demonstrated overall superiority in terms of weighted average performance as well as class-specific performance metrics. It involves using BERT to generate contextual embeddings for the input text, passing them through a CNN layer to capture local features, and feeding the resulting features into an LSTM layer for sequential modeling and final classification. The superior performance of the BERT with CNN-LSTM architecture can be attributed to several key factors. Firstly, BERT, which is a state-of-the-art pre-trained language model, excels in capturing contextual information and semantic understanding from textual data. This enables it to extract intricate patterns and nuances in the EHRs related to smoking status, which can be highly context dependent. Furthermore, the combination of CNN and LSTM layers in this architecture allows for the effective extraction of both local and sequential features from the EHR text. CNNs are adept at capturing local patterns and features, while LSTMs excel at modeling sequential dependencies. The synergistic integration of these two components enables the model to capture a wide range of relevant information, from short-term textual features to long-term contextual dependencies, making it particularly well-suited for the nuanced task of smoking status identification. The combined approach helps the model effectively capture both global contextual information and local sequential patterns, resulting in improved performance in text classification tasks compared to using BERT with classic machine learning algorithms.

However, we believe that the Embedding with CNN-LSTM demonstrated the optimal results since the discrepancy in detection rates across all classes based on confusion matrix was the narrowest gap observed across the developed architectures. The Embedding with CNN-LSTM architecture exhibited more consistent detection rates across all classes compared to BERT with CNN-LSTM. This approach, with its straightforward embeddings, ensures efficient capture of semantic meanings, leading to faster training and reduced computational demands. Moreover, when tailored to specific datasets, the embeddings can potentially offer more aligned representations for the task at hand.

The consistent detection rates exhibited by the Embedding with CNN-LSTM architecture compared to BERT with CNN-LSTM can be attributed to its more structured feature representation, simpler model complexity, and potential alignment with the dataset’s characteristics. The use of word embeddings facilitates a focused representation of text data, aiding in the consistent identification of smoking-related terms across various classes. Additionally, the Embedding with CNN-LSTM’s relative simplicity may contribute to improved generalization across classes, particularly in the presence of class imbalances. This suggests that the architecture’s suitability for the dataset, combined with effective hyperparameter tuning, plays a crucial role in achieving stable and reliable detection rates across all classes. Hence, for the collected dataset in this study and the classification goals to detect smokers (Never-Smoker, Former-Smoker, Active-Smoker), the Embedding with CNN-LSTM architecture might be the more adaptable and optimal choice.

To provide additional insight into the interpretability of our results, we explored LIME-plots from the Embedding with CNN-LSTM architecture. Notably, these plots unveiled clinically relevant top features associated with each specific class. The utilization of explainable AI methods, notably the LIME, in the developed NLP pipeline, plays a pivotal role in enhancing the interpretability and trustworthiness of our smoking status identification process within the complex landscape of EHRs. With the natural complexity of EHR data, it is essential that our AI model’s decision-making is transparent and understandable to healthcare professionals. LIME enables us to provide detailed, human-readable explanations for each prediction, highlighting the most influential features and factors that led to a specific outcome. This not only empowers clinicians to gain deeper insights into the model’s reasoning but also allows them to validate the models’ decisions against their domain expertise. By bridging the gap between AI-driven predictions and clinical understanding, the explainable AI methods contribute significantly to the credibility and reliability of our smoking status identification system in the EHR environment, ultimately adding greater confidence in its utility and accuracy. The results were discussed with domain experts, who were in favor of a balanced performance across all classes in the dataset.

Comparison to previous study results

Different studies have evaluated the application of NLP based on machine learning and deep learning techniques for the detection of smoking status through EHRs with different languages [ 12 , 14 , 39 , 40 ]. Rajendran et al. developed a binary and multiclass classification model using English EHRs from the United States [ 14 ]. The model incorporated a CNN that utilized both a word-embedding layer pre-trained from the Google news corpus and a word2vec model, resulting in superior performance compared to conventional machine learning methods. The binary classification achieved an F1-measure of 85%, while the multiclass classification reached 68% for smoking status identification. Bae et al. developed a multiclass classification model using Korean and English EHR data extracted from 4711 clinical notes [ 39 ]. The most effective model employed an unsupervised keyword extraction technique in combination with a linear support vector machine, achieving an impressive F1-score of 91% for multiclass classification. Of note, both studies encountered challenges due to limited data availability and the extensive length of patient notes. Additionally, the Korean study faced limitations in terms of the relevant corpus available for pre-training, which necessitated the use of seed keywords pre-defined by clinicians for the keyword extraction method.

To the best of our knowledge, the most comparable study to ours is one based on Swedish EHR notes [ 40 ]. It developed classic machine learning models to classify smoking status into Current-Smoker, Ex-Smoker, Non-Smoker, and Unknown. Among the 32 developed detection models, support vector machine achieved the highest F1-score of 98%. The authors did not present the performance of developed models for each of the classes, which makes it difficult to understand the ability of models in different classes. Also, they did not consider any feature extraction method to transform the text into features and capture the essential information from the text. Consequently, the reasons for models’ decisions were not presented.

Limitation and Future Work

To the best of our knowledge this study represents the first exploration of a Danish NLP-model derived from a sizable dataset of manually annotated EHR-notes, but it has some limitations. It is important to acknowledge that the models are based on constrained input data. We exclusively considered text from the relatively short and simplistic subfield associated with smoking and risk factors in the EHR systems. Applying the established models on the complete EHR note is unquestionably bound to result in a performance decrease. Nevertheless, it is worth noting that the current Danish hospital systems store information on smoking status and other risk factors in a sub-header format similar to the structure observed in this dataset.

Another limitation pertains to the absence of an “unknown” category. Following the initial data annotation process, patients with unknown smoking status were further evaluated using additional notes. Ultimately, we selected the note containing the most detailed information on smoking status, resulting in the complete exclusion of the unknown category. This, however, represents a potential drawback since the model was not trained to classify “unknown” smoking status. Finally, it would be ideal to expand the model to include more detailed information on smoking status such as smoking duration and intensity. Incorporating these factors into a model would be relevant when determining eligibility for lung cancer screening. This would require a higher standard of quality and standardization in documenting smoking status compared to the current practices.

Based on the findings of this study, we plan to further explore the potential of this algorithm on longer EHR-notes without limitations to the subfield relevant to smoking. It would be valuable to incorporate free text from general practice to identify patients at risk of lung cancer or other chronic diseases where smoking status is a significant risk factor. However, data annotation remains a time-consuming task, and the size of the dataset may be limited by this factor when dealing with larger patient notes. Additionally, there is potential to annotate other risk factors, such as alcohol consumption, to expand the current model to different outcomes beyond smoking.

Clinical perspectives

To the best of our knowledge, this is the first model based on Danish EHR data. Despite its limitations, the current model holds potential for application to Danish EHR data acquired at a hospital level. The ability to extract smoking status directly from free-text material would be highly advantageous, given that smoking status is a crucial risk factor for various acute and chronic illnesses. Having such information readily available for large patient populations allows for further investigation, as this variable is typically only accessible for specific populations such as patients with lung cancer or coronary heart disease. The incorporation of explainable AI, specifically LIME plots, opens possibilities for enhancing future models by identifying potential systematic errors. Additionally, it offers valuable insights into predictions, a crucial aspect for responsible clinicians. In addition to its potential in advancing research, this model could also find utility in screening scenarios, providing valuable information for risk assessment tools.

We present the outcomes of a novel model capable of categorizing the smoking status of patients using Danish EHRs. By combining a transformer with a convolutional neural network, specifically BERT with CNN-LSTM, we achieved a remarkable performance, with low discrepancy in detection rates across all classes. This outcome accentuates the promising possibility of classifying smoking status based on unstructured free text data. The availability of comprehensive and precise information on smoking habits could potentially prove advantageous in future research endeavors. Moreover, it can aid in identifying high-risk individuals who are eligible for screening programs such as those aimed at detecting lung cancer.

Flowchart depicting the study design in each step of the NLP pipeline. Bidirectional Encoder Representations from Transformers (BERT). Convolutional neural network with a long short-term memory layer (CNN-LSTM). K-Nearest Neighbors (KNN). Decision Tree. Created with Biorender.com

Average performance measures based on binary classification of the seven model architectures. CNN-LSTM: Convolutional neural network with a long short-term memory layer. SE: Stacking-Based Ensemble. BERT: Bidirectional Encoder Representations from Transformers. AU-ROC: Area under Receiver Operating Characteristic Curve

Confusion matrixes based on binary classification of all seven model architectures

Average performance measures based on multiclass classification of the seven model architectures. CNN-LSTM: Convolutional neural network with a long short-term memory layer. SE: Stacking-Based Ensemble. BERT: Bidirectional Encoder Representations from Transformers. AU-ROC: Area under Receiver Operating Characteristic Curve

Confusion matrixes based on multiclass classification of the architectures of all seven models

Results of the Friedman test and Nemenyi post-hoc test, α = 0.05

LIME plots representing the outcomes of multiclass classification of four distinct samples derived from Embedding with CNN-LSTM. A : Former-Smoker accurately detection with a 94% probability of being a Former-Smoker. B : Active-Smoker correctly detection with a 100% probability of being an Active-Smoker. C : Active-Smoker misclassified as a Former-Smoker. D : Smoker wrongly classified as a Non-Smoker.

Data availability

The dataset used for this study is not publicly available due to the possibility of compromising individual privacy but is available from the corresponding author on reasonable request.

Abbreviations

Natural Language Processing

Electronic Health Records

Convolutional Neural Network with a long short-term memory layer

K-Nearest Neighbors

Decision Tree

Extreme Gradient Boosting

Random Forest

Stacking-based Ensemble

Area Under the Receiver Operating Characteristics Curve

Local interpretable model-agnostic explanations

Bidirectional Encoder Representations from Transformers

True Positive

True Negative

False Positive

False Negative

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Acknowledgements

The authors would like to thank Karin Larsen, Research secretary, The Department of Oncology, Lillebaelt Hospital, University Hospital of Southern Denmark, for helping in proofreading the manuscript.

The study was funded by The Region of Southern Denmark, The University of Southern Denmark, The Danish Comprehensive Cancer Center, The Dagmar Marshall Foundation, The Beckett Foundation, The Lilly and Herbert Hansen Foundation and The Hede Nielsen Family Foundation. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Open access funding provided by University of Southern Denmark

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SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, 5230, Denmark

Ali Ebrahimi, Abdolrahman Peimankar & Uffe Kock Wiil

Department of Oncology, Lillebaelt Hospital, University Hospital of Southern Denmark, Vejle, 7100, Denmark

Margrethe Bang Høstgaard Henriksen, Torben Frøstrup Hansen & Lars Henrik Jensen

Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Vejle, 7100, Denmark

Claus Lohman Brasen

Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark

Claus Lohman Brasen, Ole Hilberg & Torben Frøstrup Hansen

Department of Internal Medicine, Lillebaelt Hospital, University Hospital of Southern Denmark, Vejle, 7100, Denmark

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Contributions

A.E designed, developed, and analyzed the methodology, performed the modeling, analyzed the results, and conducted the computations. M.B.H designed the methodology, analyzed the results, collected the data, and annotated the texts. A.E and M.B.H wrote the manuscript. C.L.B, O.H, and T.F.H contributed to analyzing the results from a clinical perspective and reviewed the manuscript. U.K.W. and A.P. contributed to the computations, result analysis and manuscript review. All authors discussed the results and contributed to the final manuscript.

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The authors are accountable for all aspects of the work and will ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Danish Data Protection Agency (19/30673, 06-12-2020) and the Danish Patient Safety Authority (3-3013-3132/1, 03-30-2020), and individual consent for this retrospective analysis was waived.

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Ebrahimi, A., Henriksen, M.B.H., Brasen, C.L. et al. Identification of patients’ smoking status using an explainable AI approach: a Danish electronic health records case study. BMC Med Res Methodol 24 , 114 (2024). https://doi.org/10.1186/s12874-024-02231-4

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• Natural language processing
• Text classification
• Stacking-based ensemble
• Deep learning
• Explainable Artificial Intelligence (XAI)
• Electronic health record
• Smoking status

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