Course: CS 6474 / CS 4803 Social Computing
Term: Spring 2022
Location: Skiles 202
Time: Monday and Wednesday 12:30 – 1:45pm Eastern Time
Virtual Office Hours: Friday 11am-12pm Eastern Time; meeting link on course Canvas
Teaching Assistant: Seunghyun (Matt) Kim
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.
- Exploration of a variety of quantitative methodologies that could be applied to the study of social computing systems.
- Building social tools that augment current social computing systems.
- Apply social data to answer questions in a variety of practical scenarios and domains, such as politics and health.
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, Perl, Java, C#). 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; however, what you hand in should be your own work. For more information, please review the Georgia Tech Honor Code.
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.
COVID-19 Protocols. The COVID-19 pandemic is still affecting the world and our lives. If you test positive for COVID-19, suspect you may have been exposed to the virus, or feel sick due to a reason that may be contagious, please email the instructor and the TA as soon as you can. To protect yourself and others in the class, we ask you to not come to the class in such a situation; you will not be penalized in any way and your grade will not be affected. Similarly, if the instructor or the TA tests positive for COVID-19, suspect exposure, or feel sick due to a suspected infection, the class will either be cancelled or held virtually (via BlueJeans or MS Teams), depending upon the specifics of the circumstances.
Finally, I highly encourage and request everyone coming to the classes to wear a well-fitting mask, and get fully vaccinated, including a booster shot; reach out to Stamps Health Services or your own health care provider if you still have questions about the safety and effectiveness of the vaccine. Although as instructor I cannot require you to follow this COVID-19 safety protocol in the classroom, as Georgia Tech President Ángel Cabrera said "I count on you to do your part, just as I know you will ask and count on one another to do the same - for your protection and for the protection of everyone around you."
Assignments and Grading
Reflections on Assigned Class Readings (any or best 10) - 25% (2.5% each)
: Piazza for reflection submission and asynchronous discussion
: Due by 11:59pm of the day before the class
: Sample reading reflections
Class Attendance/Participation - 10%
[Individual] Assignment I - 12%
[Individual] Assignment II [data | resources] - 18%
[Group] Term Project - 35%
: Project Proposal - 8%
: Project Proposal Presentation [Signup Sheet] - 5%
: Final Project Presentation [Signup Sheet] - 5%
: Final Report - 17%
Weekly Schedule *
Weekly Readings *Week 2 (Jan 19): Sociological Foundations I
An Experimental Study of the Small World Problem [pdf]
Week 3 (Jan 24): 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]
Week 4 (Jan 31): 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]
Week 4 (Feb 2): 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]
Week 5 (Feb 7): Social Computing Theories: Disclosure and Regulation
Anonymity and Self-Disclosure on Weblogs [pdf]
Understanding Social Media Disclosures of Sexual Abuse Through the Lenses of Support Seeking and Anonymity [pdf]
Week 5 (Feb 9): 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]
Week 7 (Feb 21): Analyzing Language I
Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures [pdf]
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach [pdf]
Week 7 (Feb 23): Analyzing Language II
Gender and Power: How Gender and Gender Environment Affect Manifestations of Power [pdf]
No Country for Old Members: User lifecycle and linguistic change in online communities [pdf]
Week 8 (Feb 28): 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] Preventing harassment and increasing group participation through social norms in 2,190 online science discussions [pdf]
Week 8 (Mar 2): Social System Design
Social Translucence: An Approach to Designing Systems that Support Social Processes [pdf]
The Chat Circles Series: Explorations in Designing Abstract Graphical Comm. Interfaces [pdf]
Week 9 (Mar 7): Social Computing Constructs: Credibility
Tweeting is Believing? Understanding Microblog Credibility Perceptions [pdf]
Understanding Anti-Vaccination Attitudes in Social Media [pdf]
[Optional] Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention [pdf]
Week 9 (Mar 9): Misinformation and Disinformation
Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter [pdf]
The spread of true and false news online [pdf]
[Optional] Social bots distort the 2016 US Presidential election online discussion [link]
[Optional] Social Media and Fake News in the 2016 Election [pdf]
Week 10 (Mar 14): Social Computing Constructs: Polarization and Selective Exposure
Echo Chambers Online?: Politically Motivated Selective Exposure among Internet News Users [pdf]
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]
[Optional] Partisan selective exposure in online news consumption: Evidence from the 2016 presidential campaign [pdf]
Week 10 (Mar 16): Benefits/Applications of Social Computing Systems: Health and WellBeing
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 12 (Mar 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 12 (Mar 30): Benefits/Applications of Social Computing Systems: Activism and Social Movements
The revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions [pdf]
Social media and the decision to participate in political protest: Observations from Tahrir Square [pdf]
[Optional] False equivalencies: Online activism from left to right [pdf]
Week 13 (Apr 4): Benefits/Applications of Social Computing Systems: Crisis Informatics
Crisis Informatics: Human-Centered Research on Tech & Crises [pdf]
Fifteen years of social media in emergencies: a retrospective review and future directions for crisis informatics [pdf]
[Optional] Social media and emergency preparedness in response to novel coronavirus [pdf]
Week 13 (Apr 6): Benefits/Applications of Social Computing Systems: Predictions and Forecasting I
Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear” [link]
Private traits and attributes are predictable from digital records of human behavior [link]
[Optional] Prediction and explanation in social systems [link]
Week 14 (Apr 11): Benefits/Applications of Social Computing Systems: Predictions and Forecasting II
Exploring Limits to Prediction in Complex Social Systems [pdf]
The parable of Google Flu: traps in big data analysis [pdf]
Week 14 (Apr 13): 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]
Week 15 (Apr 18): Challenges of Social Computing Systems: Privacy
Data, privacy, and the greater good [pdf]
Beyond the Belmont principles: Ethical challenges, practices, and beliefs in the online data research community [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.