Course: CS 6474 Social Computing
Term: Fall 2014
Location: ES&T L1125
Time: Monday & Wednesday 4:35 – 5:55pm
Office Hours: Thursday 11 am-12 noon (TSRB 231), or by appointment
Teaching Assistant: Joseph Gonzales
Overview
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.
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 three different 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, 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, and each group will be required to make a midterm presentation and a final presentation of their work. They will also need to submit a midterm report outlining their work as well as a final report. 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, list of which will be made available in course material. 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 data mining. An overview of the concepts and tools needed will be reviewed across two lectures, 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, C#). Experience in use of a scientific computing software like R or MATLAB is a bonus.
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.
Assignments and Grading
Responses to Class Readings (on Piazza) - 10%
Assignment I/ Make-up Assignment - 10%
Assignment II - 10%
Assignment III - 10%
Term Project Phase I Report (due at midterm) - 20%
In-class Presentation of Term Project Phase I - 5%
Term Project Phase II Report (due at final) - 20%
In-class Presentation of Term Project Phase II - 5%
Class Participation - 10%
Weekly Schedule *
| Week 1 (18-Aug) | Introduction |
| Week 1 (20-Aug) | Background |
| Week 2 (25-Aug) | SNS Overview |
| Week 2 (27-Aug) | Social Media Overview |
| Week 3 (1-Sep) | Labor Day - No class |
| Week 3 (3-Sep) | Social System Design |
| Week 4 (8-Sep) | Term Project Discussion |
| Week 4 (10-Sep) | Statistics Review |
| Week 5 (15-Sep) | Data Mining Review I |
| Week 5 (17-Sep) | Data Mining Review II |
| Week 6 (22-Sep) | Text (Polarity/Affect) |
| Week 6 (24-Sep) | Text (Demographics, Communities) |
| Week 7 (29-Sep) | Social Multimedia |
| Week 7 (1-Oct) | Networks (Ties) |
| Week 8 (6-Oct) | Networks (Structure) |
| Week 8 (8-Oct) | Networks (Time) |
| Week 9 (13-Oct) | Fall Recess - No class |
| Week 9 (15-Oct) | Term Project Phase I Presentations |
| Week 10 (20-Oct) | Social Capital and Self-Esteem |
| Week 10 (22-Oct) | Personality and Behavior |
| Week 11 (27-Oct) | Disclosure and Regulation |
| Week 11 (29-Oct) | Trust |
| Week 12 (3-Nov) | Credibility |
| Week 12 (5-Nov) | Polarization and Selective Exposure |
| Week 13 (10-Nov) | Trends and Forecasting |
| Week 13 (12-Nov) | Event and News Analytics |
| Week 14 (17-Nov) | Location and Mobility |
| Week 14 (19-Nov) | No class, attend keynote talk at the Atlanta CSS Workshop (Nov 21) |
| Week 15 (24-Nov) | Microblogging and Collaboration |
| Week 15 (26-Nov) | Privacy |
| Week 16 (1-Dec) | Term Project Phase II Presentations |
| Week 16 (3-Dec) | Term Project Phase II Presentations |
Weekly Readings *
Week 1 (20-Aug): BackgroundSocial Machines: Computing means Connecting [link]
Week 2 (25-Aug): SNS Overview
Computer Networks as Social Networks [pdf]
Social Network Sites: Definition, History, and Scholarship [pdf]
Friendster and Publicly Articulated Social Networks [pdf]
Week 2 (27-Aug): Social Media Overview
Why We Twitter: Understanding Microblogging Usage and Communities [pdf]
Measuring User Influence in Twitter: The Million Follower Fallacy [pdf]
Is It Really About Me?: Message Content in Social Awareness Streams [pdf]
Week 3 (3-Sep): 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]
Visualizing Email Content: Portraying Relationships from Conversational Histories [pdf]
Week 6 (22-Sep): Text (Polarity/Affect)
Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures [pdf]
Not All Moods Are Created Equal! Exploring Human Emotional States in Social Media [pdf]
Modeling Public Mood and Emotion: Twitter Sentiment and Socioeconomic Phenomena [pdf]
Week 6 (24-Sep): Text (Demographics, Communities)
Gender Identity and Lexical Variation in Social Media [pdf]
No Country for Old Members: User lifecycle and linguistic change in online communities [pdf]
Mark My Words! Linguistic Style Accommodation in Social Media [pdf]
Week 7 (29-Sep): Social Multimedia
How Flickr Helps us Make Sense of the World [pdf]
Faces Engage Us: Photos with Faces Attract More Likes and Comments on Instagram [pdf]
What We Instagram: A First Analysis of Instagram Photo Content and User Types [pdf]
Week 7 (1-Oct): Networks (Ties)
The Strength of Weak Ties [pdf]
Predicting Tie Strength With Social Media [pdf]
Signed Networks in Social Media [pdf]
Week 8 (6-Oct): Networks (Structure)
An Experimental Study of the Small World Problem [pdf]
Planetary-Scale Views on a Large Instant-Messaging Network [pdf]
The Political Blogosphere and the 2004 U.S. Election: Divided They Blog [pdf]
Week 8 (8-Oct): Networks (Time)
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations[pdf]
Group Formation in Large Social Networks: Membership, Growth, and Evolution [pdf]
The Life and Death of Online Groups: Predicting Group Growth and Longevity [pdf]
Week 10 (20-Oct): Social Capital and Self-Esteem
The Benefits of Facebook “Friends:” Social Capital and College Students' Use of Online SNS [pdf]
Social Capital on Facebook: Differentiating Uses and Users [pdf]
Mirror, Mirror on My Facebook Wall: Effects of Exposure to Facebook on Self-Esteem [pdf]
Week 10 (22-Oct): Personality and Behavior
The Relationship Between Number of Friends and Interpersonal Impressions on Facebook [pdf]
Facebook Profiles Reflect Actual Personality, not Self-Idealization [pdf]
Private Traits and Attributes are Predictable from Digital Records of Human Behavior [pdf]
Week 14 (27-Oct): Disclosure and Regulation
Anonymity and Self-Disclosure on Weblogs [pdf]
Taking Risky Opportunities in Youthful Content Creation: Teenagers' Use of SNS for Intimacy, Privacy and Self-Expression [pdf]
The Presentation of Self in the Age of Social Media [pdf]
Week 11 (29-Oct): Trust
Trust Breaks Down in Electronic Contexts but Can Be Repaired by Some Initial F2F Contact [pdf]
Inferring Binary Trust Relationships in Web-based Social Networks [pdf]
Twitter Under Crisis: Can We Trust What We RT? [pdf]
Week 12 (3-Nov): Credibility
Information Credibility on Twitter [pdf]
Tweeting is Believing? Understanding Microblog Credibility Perceptions [pdf]
Finding and Assessing Social Media Information Sources in the Context of Journalism [pdf]
Week 12 (5-Nov): Polarization and Selective Exposure
Echo Chambers Online?: Politically Motivated Selective Exposure among Internet News Users [pdf]
Presenting Diverse Political Opinions: How and How Much [pdf]
Dynamic Debates: An Analysis of Group Polarization Over Time on Twitter [pdf]
Week 13 (10-Nov): Trends and Forecasting
Rhythms of Social Interaction: Messaging Within a Massive Online Network [pdf]
On the Study of Diurnal Urban Routines on Twitter [pdf]
Predicting the Future With Social Media [pdf]
Week 13 (12-Nov): Event and News Analytics
Characterizing Debate Performance via Aggregated Twitter Sentiment [pdf]
What is Twitter, a Social Network or a News Media? [pdf]
Hip and Trendy: Characterizing Emerging Trends on Twitter [pdf]
Week 14 (17-Nov): Location and Mobility
The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City [pdf]
The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes [pdf]
Tweets from Justin Bieber's Heart: The Dynamics of the Location Field in User Profiles [pdf]
Week 14 (19-Nov): Social Search and Q&A
What Do People Ask Their Social Networks, and Why? [pdf]
SearchBuddies: Bringing Search Engines into the Conversation [pdf]
Finding High-Quality Content in Social Media [pdf]
Week 15 (24-Nov): Microblogging and Collaboration
Beyond Microblogging: Conversation and Collaboration via Twitter [pdf]
Voluntweeters: Self-Organizing by Digital Volunteers in Times of Crisis [pdf]
Diversity Among Enterprise Online Communities: Collaborating, Teaming, and Innovating Through Social Media [pdf]
Week 15 (26-Nov): Privacy
Information Revelation and Privacy in Online Social Networks [pdf]
Facebook Privacy Settings: Who Cares? [pdf]
Facebook, Youth and Privacy in Networked Publics [pdf]
Recommended, Relevant Readings
Not required, but the following books are good references for the class:
Networks, Crowds, and Markets, by David Easley and Jon Kleinberg
Six Degrees, by Duncan Watts
On Individuality and Social Forms, by Georg Simmel
Networked, by Barry Wellman
The Signal and the Noise, by Nate Silver
Writing for Social Scientists, by Howard Becker
Machine Learning for Hackers, by Drew Conway and John Myles White
Natural Language Processing with Python, by Steven Bird, Ewan Klein, and Edward Loper
Pattern Classification, by Richard Duda, Peter Hart, and David Stork
Related Courses
[Previous offering of the course] Social Computing, offered by Eric Gilbert at Georgia Tech
Computational Social Science, offered by Jacob Eisenstein at Georgia Tech
The Structure of Information Networks, offered by Jon Kleinberg at Cornell
Networks, offered by Lada Adamic at University of Michigan/Coursera
Network Analysis and Modeling, offered by Aaron Clauset at University of Colorado
Social and Information Network Analysis, offered by Jure Leskovec at Stanford
Fnds of Social Computing, offered by Kate Larson at University of Waterloo
Social Computing, offered by Yiling Chen at Harvard
Social Computing, offered by Irwin King at University of Berkeley
* Topics to be covered and the corresponding readings are subject to change. Please always check the online schedule.