Course: CS 6474 / CS 4803 Social Computing
Term: Spring 2024
Location: Klaus Advanced Computing 2447
Time: Monday and Wednesday 12:30 – 1:45pm Eastern Time
Virtual Office Hours: By appointment; meeting link on course Canvas
Teaching Assistants: Seunghyun (Matt) Kim and Sachin Pendse
Piazza: Link on course Canvas


This course is geared toward developing a broad understanding of the characteristics of today’s online social systems, including the opportunities and challenges that engender this emergent area. We will focus on the study of different social processes, behavior, and context on today's online social platforms, and learn how to make sense of the vast repositories of data that are generated on these platforms everyday. We will also learn about the design principles behind these systems and the key issues that arise from the widespread adoption of social computing systems in the wild. Learning objectives include:

- Collection and analysis of large-scale social data, drawing on principled theoretical underpinnings.
- Exploration of a variety of quantitative methodologies that could be applied to the study of social computing systems, identifying the strength and pitfalls of each.
- Building social tools that augment current social computing systems.
- Applying social data to answer questions in a variety of practical scenarios and domains, such as politics and health.
- Learning to unpack and potentially mitigate the harms and problems in contemporary social computing systems.
- Identifying and understanding the varied ethical challenges in the study of social computing systems and in the analysis of their data.

The course will be taught seminar style, which means there will be weekly readings on a variety of topics (see topics and schedule below), and students will be required to participate in a group term project. There will be no exams, however there are going to be individual assignments which will involve mini individual projects. Students will also be required to participate in discussions on the pre-assigned class readings in a blog (Piazza), in order to demonstrate their understanding of the material, and to raise interesting questions and points for class discussion.

The term project will be 3-4 person group projects. Each student will need to clearly articulate their concrete contribution in the group project. Topic of the project can be picked by the student groups after discussion with the instructor; the instructor will also provide a set of sample project ideas in class materials. If the project requires data analysis, a contribution of the project could be collecting that data, or the students could also use any of the publicly available social datasets available online. Each project will require both original work as well as a small number of compulsory analyses that cover key concepts from the course.

Students may audit the course, but all students who attend must perform the weekly blog posts about the reading, to facilitate discussion.

Required Skills: In terms of prerequisite skills, students need to have basic knowledge of statistics and preliminary machine learning. An overview of the concepts and tools needed will be reviewed as needed, however in-depth coverage of the fundamentals is not in the scope of this course. Students also need to be proficient in programming, in an object-oriented/scripting language (e.g., Python). Experience in use of a scientific computing software like R is a bonus.

Late Policy. Students need to submit all of their materials on or before the deadline to qualify for 100% credit. For the assignments and term project proposal, milestone report, and final report, 24 hours delay will result in 25% penalty; 48 hours late submissions will incur 50% penalty. Materials submitted past 48 hours will not be accepted, and will entered a zero grade. Check the course syllabus for the deliverables on which late policy is applicable.

Academic Integrity. All assigned work is expected to be individual, except where explicitly indicated otherwise. You are encouraged to discuss the assignments with your classmates (or AI; more below); however, what you hand in should be your own work. For more information, please review the Georgia Tech Honor Code. We will be checking for plagiarism issues (using in-built tools on Canvas) and any instance will be dealt with the Institute's recommended protocol, such as reporting to the Office of Student Integrity.

Policy on Using AI Technologies. This policy is adapted from one by David Joyner.

We treat AI-based assistance, such as ChatGPT, the same way we treat collaboration with other people: you are welcome to talk about your ideas and work with other people, both inside and outside the class, as well as with AI-based assistants. However, all work you submit must be your own. You should never include in your assignment anything that was not written directly by you without proper citation (including quotation marks and in-line citation for direct quotes). Including anything you did not write in your assignment without proper citation will be treated as an academic misconduct case.

If you are unsure where the line is between collaborating with AI and copying from AI, we recommend the following heuristics:

- Never hit "Copy" within your conversation with an AI assistant. You can copy your own work into your conversation, but do not copy anything from the conversation back into your assignment. Instead, use your interaction with the AI assistant as a learning experience, then let your assignment reflect your improved understanding.

- Do not have your assignment and the AI agent open at the same time. Similar to above, use your conversation with the AI as a learning experience, then close the interaction down, open your assignment, and let your assignment reflect your revised knowledge. This heuristic includes avoiding using AI directly integrated into your composition environment: just as you should not let a classmate write content or code directly into your submission, so also you should avoid using tools that directly add content to your submission.

Deviating from these heuristics does not automatically qualify as academic misconduct; however, following these heuristics essentially guarantees your collaboration will not cross the line into misconduct.

Use of grammar checkers and spelling verifiers are allowed without restriction.

Mental Health. As college students, it can be hard to prioritize your health, especially when you are pushed to prioritize academics, work, and extracurricular activities. The instructor is happy to talk to you privately if you need mental health related accommodations. Please also refer to the various campus resources to access timely, professional help as well as self-care tips.

Assignments and Grading

Reflections on Assigned Class Readings (any or best 10) - 25% (2.5% each)
      : Piazza for reflection submission and asynchronous discussion (link on course Canvas)
      : Due by 11:59pm of the day before the class
      : Sample reading reflections
Class Attendance/Participation - 10%
[Individual] Assignment I - 5%
[Individual] Assignment II - 10%
[Individual] Assignment III [data | resources] - 17%
[Group] Term Project - 33%
      : Project Proposal - 7%
      : Project Proposal Presentation - 3%
      : Final Project Presentation - 5%
      : Final Report - 18%

Weekly Schedule *

Week 1 (Jan 8) Introduction
Week 1 (Jan 10) Sociological Foundations I
Assignment I released
Week 2 (Jan 15) MLK Day
Week 2 (Jan 17) Sociological Foundations II
Recorded lecture; no in-person class
Discussion of example term projects
Week 3 (Jan 22) Sociological Foundations III
Discussion of specs for term project proposals
Week 3 (Jan 24) Social Computing Theories: Social Capital and Social Influence
Assignment I due
Week 4 (Jan 29) Social Computing Theories: Public Displays and Performance
Week 4 (Jan 31) Social Computing Theories: Identity
Week 5 (Feb 5) Term Project Proposal Presentations I
Week 5 (Feb 7) Term Project Proposal Presentations II
Term project proposals due
Week 6 (Feb 12) Computational Methods for Social Computing I (Guest Lecture by Mohit Chandra)
Week 6 (Feb 14) Computational Methods for Social Computing II
Assignment II released
Week 7 (Feb 19) Bridging the Offline and the Online: Language
Week 7 (Feb 21)Bridging the Offline and the Online: Activism and Social Movements
Week 8 (Feb 26)Bridging the Offline and the Online: Communities (Guest Lecture by Sanjay Kairam)
Week 8 (Feb 28) Benefits/Applications of Social Computing Systems: Politics
Week 9 (Mar 4) Benefits/Applications of Social Computing: Collaboration (Guest Lecture by Soya Park)
Week 9 (Mar 6) Benefits/Applications of Social Computing Systems: Health and Well-Being
Assignment II due
Assignment III released
Week 10 (Mar 11) Benefits/Applications of Social Computing Systems: Culture and Online Social Support (Guest Lecture by Sachin Pendse)
Week 10 (Mar 13) Problems of Social Computing Systems: Online Abuse and Hate Speech
Week 11 (Mar 18) Spring Semester Recess
Week 11 (Mar 20) Spring Semester Recess
Week 12 (Mar 25) Problems of Social Computing Systems: Misinformation and Disinformation
Week 12 (Mar 27) Problems of Social Computing Systems: Polarization and Selective Exposure
Week 13 (Apr 1)Tackling Harms: Online Content Moderation
Week 13 (Apr 3) Tackling Harms: Methodological Pitfalls
Discussion of Specs for Final Project Deliverables
Week 14 (Apr 8) Social Computing and Sociolinguistics (Guest Lecture by Minje Choi)
Assignment III due
Week 14 (Apr 10) Challenges of Social Computing Systems: Ethics
Week 15 (Apr 15)Final Term Project Presentations
Week 15 (Apr 17) Final Term Project Presentations
Week 16 (Apr 22) Looking Ahead: Social Computing and Generative AI

Weekly Readings *

Week 1 (Jan 10): Sociological Foundations I
An Experimental Study of the Small World Problem [pdf]

Week 2 (Jan 17): Sociological Foundations II
Structural Holes and Good Ideas [pdf]

Week 3 (Jan 22): Sociological Foundations III
The Strength of Weak Ties [pdf]
Predicting Tie Strength With Social Media [pdf]
[Optional] The strength of weak ties revisited: Further evidence of the role of strong ties in the provision of online social support [pdf]

Week 3 (Jan 24): Social Computing Theories: Social Capital and Social Influence
The Benefits of Facebook “Friends:” Social Capital and College Students' Use of Online SNS [pdf]
Everyone's an influencer: Quantifying Influence on Twitter [pdf]
[Optional] Social network site affordances and their relationship to social capital processes [pdf]
[Optional] Psychological Well-Being and Social Media Use: A Meta-Analysis of Associations between Social Media Use and Depression, Anxiety, Loneliness, Eudaimonic, Hedonic and Social Well-Being [pdf]

Week 4 (Jan 29): Social Computing Theories: Public Displays and Performance
The Presentation of Self in Everyday Life: Introduction (PDF file pgs. 6-10) [pdf]
The Presentation of Self in the Age of Social Media: Distinguishing Performances and Exhibitions Online [pdf]
[Optional] The algorithmic crystal: Conceptualizing the self through algorithmic personalization on TikTok[pdf]

Week 4 (Jan 31): Social Computing Theories: Identity
Identity and Deception in the Virtual Community [pdf]
4chan and/b: An Analysis of Anonymity and Ephemerality in a Large Online Community [pdf]
[Optional] "This is a Throwaway Account" Temporary Technical Identities and Perceptions of Anonymity in a Massive Online Community [pdf]
[Optional] Understanding Social Media Disclosures of Sexual Abuse Through the Lenses of Support Seeking and Anonymity [pdf]

Week 7 (Feb 19): Bridging the Offline and the Online: Language
Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures [pdf]
Modeling Stress with Social Media Around Incidents of Gun Violence on College Campuses [pdf]
[Optional] Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach [pdf]
[Optional] Semantics Derived Automatically from Language Corpora Contain Human-Like Biases [pdf]

Week 7 (Feb 21): Bridging the Offline and the Online: Activism and Social Movements
The Revolutions were Tweeted: Information Flows During the 2011 Tunisian and Egyptian Revolutions [pdf]
Social Media Participation in an Activist Movement for Racial Equality [pdf]
[Optional] False Equivalencies: Online Activism from Left to Right [pdf]
[Optional] Activism or Slacktivism? The Potential and Pitfalls of Social Media in Contemporary Student Activism [pdf]

Week 8 (Feb 26): Bridging the Offline and the Online: Communities
From Virtual Strangers to IRL Friends: Relationship Development in Livestreaming Communities on Twitch) [pdf]
A social-ecological approach to modeling sense of virtual community (SOVC) in livestreaming communities [pdf]

Week 8 (Feb 28): Benefits/Applications of Social Computing Systems: Politics
The Political Blogosphere and the 2004 U.S. Election: Divided They Blog [pdf]
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment [pdf]
[Optional] What is Twitter, a Social Network or a News Media? [pdf]
[Optional] "I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper" - A Balanced Survey on Election Prediction using Twitter Data [pdf]
[Optional] Characterizing social media manipulation in the 2020 US presidential election [pdf]

Week 9 (Mar 4): Benefits/Applications of Social Computing Systems: Collaboration
Thinking Assistants: LLM-Based Conversational Assistants that Help Users Think By Asking rather than Answering [pdf]

Week 9 (Mar 6): Benefits/Applications of Social Computing Systems: Health and Well-Being
Predicting Depression via Social Media [pdf]
Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic Signals [pdf]
[Optional] How Social Media will Change Public Health [pdf]
[Optional] Facebook Language Predicts Depression in Medical Records [pdf]

Week 10 (Mar 11): Benefits/Applications of Social Computing Systems: Culture and Online Social Support
Moments of Change: Analyzing Peer-Based Cognitive Support in Online Mental Health Forums [pdf]
From Treatment to Healing: Envisioning a Decolonial Digital Mental Health [pdf]

Week 10 (Mar 13): Problems of Social Computing Systems: Online Abuse, Harassment, and Hate Speech
Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions [pdf]
Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media During the COVID-19 Crisis [pdf]
[Optional] When Online Harassment is Perceived as Justified [pdf]

Week 12 (Mar 25): Problems of Social Computing Systems: Misinformation and Disinformation
The spread of true and false news online [pdf]
Quantifying the Impact of Misinformation and Vaccine-Skeptical Content on Facebook [pdf]
[Optional] Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter [pdf]
[Optional] Social bots distort the 2016 US Presidential election online discussion [link]

Week 12 (Mar 27): Problems of Social Computing Systems: Polarization and Selective Exposure
Exposure to opposing views on social media can increase political polarization [pdf]
Like-minded sources on Facebook are prevalent but not polarizing [pdf]
[Optional] Exposure to ideologically diverse news and opinion on Facebook [pdf]
[Optional] “I always assumed that I wasn’t really that close to [her]”: Reasoning about invisible algorithms in the news feed [pdf]

Week 13 (Apr 1): Tackling Harms: Online Content Moderation
You Can't Stay Here: The Efficacy of Reddit's 2015 Ban Examined Through Hate Speech [pdf]
#thyghgapp: Instagram content moderation and lexical variation in pro-eating disorder communities [pdf]
[Optional] The effect of moderation on online mental health conversations [pdf]
[Optional] Deplatforming did not decrease Parler users’ activity on fringe social media [pdf]
[Optional] Preventing harassment and increasing group participation through social norms in 2,190 online science discussions [pdf]
[Optional] Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support [pdf]
[Optional] The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic [pdf]

Week 13 (Apr 3): Tackling Harms: Methodological Pitfalls
The parable of Google Flu: traps in big data analysis [pdf]
Exploring Limits to Prediction in Complex Social Systems [pdf]
[Optional] Private traits and attributes are predictable from digital records of human behavior [link]
[Optional] Do datasets have politics? Disciplinary values in computer vision dataset development [pdf]
[Optional] Who is the "human" in human-centered machine learning: The case of predicting mental health from social media [pdf]

Week 14 (Apr 8): Social Computing and Sociolinguistics
Ten Social Dimensions of Conversations and Relationships [pdf]
Analyzing the Engagement of Social Relationships during Life Event Shocks in Social Media [pdf]
[Optional] More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks [pdf]

Week 14 (Apr 10): Challenges of Social Computing Systems: Ethics
Experimental evidence of massive-scale emotional contagion through social networks [pdf]
A taxonomy of ethical tensions in inferring mental health states from social media [pdf]
[Optional] Unexpected expectations: Public reaction to the Facebook emotional contagion study [pdf]
[Optional] "We Are the Product": Public Reactions to Online Data Sharing and Privacy Controversies in the Media [pdf]

Week 16 (Apr 22): Looking Ahead: Social Computing and Generative AI
Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions [pdf]
Can Large Language Models Transform Computational Social Science? [pdf]

* Topics to be covered and the corresponding readings are subject to change. Please always check the online schedule.