Course: CS 6474 / CS 4803 Social Computing
Term: Spring 2026
Location: Klaus Advanced Computing | Room 1456
Time: Monday and Wednesday 12:30 – 1:45pm Eastern Time
Virtual Office Hours: By appointment; meeting link on course Canvas
Teaching Assistants: Shravika Mittal and Johnny Nguyen
Overview
This course provides a comprehensive introduction to social computing, focusing on how online social systems are designed, used, measured, and governed in practice. We examine contemporary social platforms, such as social media, online communities, and peer-production systems, as complex socio-technical ecosystems shaped by human behavior, algorithmic mediation, and institutional and cultural forces. Through a combination of readings, discussions, empirical assignments, and project-based work, students will develop a deep understanding of both the opportunities these systems create and the challenges they pose.
The course will be taught seminar style. Each week, students will engage with readings spanning core topics in social computing (see the topics and schedule below) and will be expected to come prepared to discuss, critique, and synthesize ideas in class. There will be no exams. Instead, students will complete individual homeworks structured as mini-projects that develop practical skills in working with social data and applying course concepts. Students are also expected to complete topical assignments on the assigned readings that assess comprehension and demonstrate engagement in the classroom.
A central emphasis of the course is learning how to reason about and analyze large-scale social data generated through online interaction. Students will gain experience collecting, processing, and analyzing real-world social data, grounding their analyses in relevant social science theories while critically examining the assumptions, limitations, and biases of different data sources and methods. The course introduces a range of quantitative and computational approaches, including descriptive analysis, network analysis, text and behavioral modeling, and causal and quasi-experimental reasoning -- highlighting when each approach is appropriate and what kinds of questions it can (and cannot) answer.
Beyond analysis, the course explores the design and evaluation of social computing systems themselves. Students will study design principles underlying social platforms and build or prototype social tools that augment existing systems, with attention to usability, incentives, and unintended consequences. Application domains such as civic engagement, politics, health, and well-being are used throughout the course to ground technical concepts in societally meaningful contexts.
A major component of the course is a term project completed in teams of 3-5 students. Project topics may be proposed by student teams in consultation with the instructor, and the instructor will also provide a set of sample project ideas in the course materials. Projects may involve original data collection and analysis, or may leverage publicly available social datasets. Each student is expected to clearly articulate and execute a concrete contribution to the team project. In addition to original work, projects will include a small number of required analyses that ensure coverage of key course concepts and methods.
Finally, the course foregrounds ethical, social, and policy considerations central to social computing research and practice. Students will critically engage with issues of privacy, consent, fairness, harm, representation, and power, and learn strategies for identifying, measuring, and mitigating harms arising from online social systems. By the end of the course, students will be equipped not only with methodological skills, but also with the conceptual and ethical frameworks needed to responsibly study and shape social technologies in the real world.
Learning Objectives
- Collect, manage, and analyze large-scale social data using principled, theory-informed approaches.
- Apply and critically evaluate a range of quantitative and computational methods for studying online social systems, understanding their strengths, limitations, and tradeoffs.
- Design and prototype social computing tools that meaningfully augment or intervene in existing social platforms.
- Use social data to address substantive questions in domains such as politics, health, and well-being.
- Identify, analyze, and propose strategies to mitigate harms and unintended consequences in contemporary social computing systems.
- Recognize and reason about ethical challenges in social computing research and practice, including issues of privacy, equity, and responsible data use.
Course Logistics
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).
Late Policy. Students need to submit all of their materials on or before the deadline to qualify for 100% credit. For the homeworks, upto a 24 hour 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. This late policy does not apply to term project deliverables or the class reading assignments.
Institute-Approved Absences. Students who miss class due to Institute-aproved activities (e.g., field trips, professional conferences, or athletic events) will be permitted to make up missed work upon presentation of official documentation from the Office of the Registrar. In such cases, the student is asked to inform the instructor ahead of time. The instructor will work with the student to establish reasonable deadlines and/or alternative arrangements for completing the missed coursework, as communicated directly to the student.
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.
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 or state-of-the-art AI plagiaarism detection tools) and instances may be dealt with the Institute's recommended protocol, such as reporting to the Office of Student Integrity.
This course follows the Georgia Tech Student-Faculty Expectations Agreement, which outlines standards for respectful engagement, academic integrity, and mutual responsibilities. For complete details, see the full agreement here.
Accessibility and Disability Accommodations. Georgia Tech is committed to providing an inclusive and accessible learning environment for all students. If you have, or think you may have, a disability and require academic accommodations (including classroom access, assignment adjustments, or exam support), please register with the Office of Disability Services (ODS) and provide me your Faculty Notification/Accommodation Letter as early in the semester as possible so we can review and implement approved accommodations collaboratively. The Office of Disability Services partners with students and faculty to ensure reasonable accommodations under the Americans with Disabilities Act and Section 504 of the Rehabilitation Act; for more information on the process and available resources, see here. The campus also provides various mental health related resources to access timely, professional help as well as to learn strategies for self-care.
Assignments and Grading
[Individual] Assignments on Class Readings (due anytime after the particular class till the last class of instructional period on April 27) - 35%
: (Sociological Foundations) Class Reading Assignment 1 - 6%
: (Social Computing Theories) Class Reading Assignment 2 - 6%
: (Bridging Offline and Online) Class Reading Assignment 3 - 4%
: (Benefits of Social Computing Systems) Class Reading Assignment 4 - 4%
: (Problems of Social Computing Systems) Class Reading Assignment 5 - 7%
: (Tackling Harms) Class Reading Assignment 6 - 4%
: (Methodological Pitfalls and Solutions) Class Reading Assignment 7 - 4%
[Individual] Homeworks - 30%
: Homework I - 5%
: Homework II - 10%
: Homework III - 15%
[Group] Term Project - 35%
: Project Proposal - 7%
: Project Proposal Presentation [Signup Form] - 3%
: Final Project Presentation and Oral Exam (due during the final exam week) - 7%
: Final Report (due during the final exam week) - 18%
Grading Scale: Final course grades will follow a standard scale: A = 90-100%, B = 80-89%, C = 70-79%, D = 60-69%, and F = below 60%.
Weekly Schedule *
| Week 1 (Jan 12) | Introduction |
| Week 1 (Jan 14) | Sociological Foundations I |
| Homework I released | |
| Week 2 (Jan 19) | MLK Day |
| Week 2 (Jan 21) | Sociological Foundations II |
| Discussion of example term projects | |
| Week 3 (Jan 26) | Sociological Foundations III |
| Class Reading Assignment 1 available | |
| Term project ideation and team matchmaking | |
| Week 3 (Jan 28) | Social Computing Theories: Social Capital and Social Influence |
| Homework I due | |
| Discussion of specs for term project proposals | |
| Week 4 (Feb 2) | Social Computing Theories: Public Displays and Performance |
| Week 4 (Feb 4) | Social Computing Theories: Identity |
| Class Reading Assignment 2 available | |
| Week 5 (Feb 9) | Term Project Proposal Presentations I |
| Week 5 (Feb 11) | Term Project Proposal Presentations II |
| Term project proposals due | |
| Week 6 (Feb 16) | Bridging the Offline and the Online: Language |
| Week 6 (Feb 18) | Bridging the Offline and the Online: Activism and Social Movements |
| Class Reading Assignment 3 available | |
| Homework II released | |
| Week 7 (Feb 23) | Benefits/Applications of Social Computing Systems: Health and Well-Being |
| Week 7 (Feb 25) | Benefits/Applications of Social Computing Systems: Politics |
| Class Reading Assignment 4 available | |
| Week 8 (Mar 2) | Computational Methods for Social Computing I (Guest Lecture by Mohit Chandra) |
| Week 8 (Mar 4) | Computational Methods for Social Computing II |
| Week 9 (Mar 9) | Problems of Social Computing Systems: Online Abuse and Hate Speech |
| Week 9 (Mar 11) | Problems of Social Computing Systems: Misinformation and Disinformation |
| Homework II due | |
| Week 10 (Mar 16) | Problems of Social Computing Systems: Polarization and Selective Exposure |
| Week 10 (Mar 18) | Problems of Social Computing Systems: Information Quality and Bias (Guest Lecture by Jiawei Zhou) |
| Class Reading Assignment 5 available | |
| Homework III released | |
| Week 11 (Mar 23) | Spring Semester Recess |
| Week 11 (Mar 25) | Spring Semester Recess |
| Week 12 (Mar 30) | Tackling Harms: Content Moderation |
| Week 12 (Apr 1) | Tackling Harms: Algorithmic Influence and Platform Effects (Guest Lecture TBD) |
| Class Reading Assignment 6 available | |
| Week 13 (Apr 6) | Methodological Pitfalls and Solutions I |
| Week 13 (Apr 8) | Methodological Pitfalls and Solutions II |
| Discussion of Final Project Presentation Specs and Schedule | |
| Class Reading Assignment 7 available | |
| Week 14 (Apr 13) | Interdisciplinary Perspectives and Approaches to Social Computing (Guest Lecture TBD) |
| Week 14 (Apr 15) | Looking Ahead: Social Computing and Generative AI |
| Homework III due | |
| Week 15 (Apr 20) | Final Term Project Preparation; No In-Person Class |
| Week 15 (Apr 22) | Final Term Project Preparation; No In-Person Class |
| Week 16 (Apr 27) | Persistent Challenges, Current and Future: Ethics |
Weekly Readings *
Week 1 (Jan 14): Sociological Foundations IAn Experimental Study of the Small World Problem [pdf]
Week 2 (Jan 21): Sociological Foundations II
Structural Holes and Good Ideas [pdf]
Week 3 (Jan 26): 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 28): 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 (Feb 2): 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 (Feb 4): 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 6 (Feb 16): 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 6 (Feb 18): 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 7 (Feb 23): 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 7 (Feb 25): 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 9): 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 9 (Mar 11): 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 10 (Mar 16): 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 10 (Mar 18): Problems of Social Computing Systems: Information Quality and Bias
Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions [pdf]
Practicing Information Sensibility: How Gen Z Engages with Online Information [pdf]
[Optional] A Framework of Severity for Harmful Content Online [pdf]
[Optional] Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking [pdf]
Week 12 (Mar 30): 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 12 (Apr 1): Tackling Harms: Algorithmic Influence and Platform Effects
TBA
Week 13 (Apr 6): Tackling Harms: Methodological Pitfalls and Solutions I
The parable of Google Flu: traps in big data analysis [pdf]
Exploring Limits to Prediction in Complex Social Systems [pdf]
Week 13 (Apr 8): Tackling Harms: Methodological Pitfalls and Solutions II
Private traits and attributes are predictable from digital records of human behavior [link]
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 13): Interdisciplinary Perspectives and Approaches to Social Computing
TBA
Week 14 (Apr 15): Looking Ahead: Social Computing and Generative AI
Can Large Language Models Transform Computational Social Science? [pdf]
Generative Agents: Interactive Simulacra of Human Behavior [pdf]
Week 16 (Apr 27): Persistent Challenges, Current and Future: 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]
* Topics to be covered and the corresponding readings are subject to change. Please always check the online schedule.