DESCRIPTION:
This course focuses on how social groups form and evolve, how members of these groups interact with each other, and how these groups are supported and augmented with computer systems. The course is interdisciplinary, drawing from the fields of computer science, psychology, and sociology. It covers key theories and technologies of social computing in terms of (1) computer systems supporting social behavior and (2) socially intelligent computing carried out by groups. Students will have a chance to explore social computing systems, get experience with social data analyses and focus on design, and evaluation of a social software as their final project for the course.
PREREQUISITES:
This course does not assume any particular prerequisites. However, this is a graduate course which assumes critical thinking, desire to learn and being challenged with new topics, and hard work.
TEXT:
We will be reading excerpts from a large number of books and articles. Links to electronic copies are provided.
GRADING:
- In-class activities [10 points]
- 3 Quizzes [15 points]: Social network analysis, social capital, Recommender systems
- Readings and response to readings [15 points]
- Social data analysis assignment [20 points]
- Social software analysis assignment [10 points]
- Final project [30 points]
OFFICE HOUR:
Monday 3:00-5:00 pm, or by appointment, 709 Information Science Building (135 North Bellefield Avenue)
TOPICS TO BE COVERED:
- Social software
- Social computing technologies
- Social information processing
- Human computation and collective intelligence
- Social capital
- Online communities
- Socialization of newcomers
- Encouraging contribution
- Diversity, conflict and coordination
- Evaluation methodologies
- Visualization and sense making
- Research ethics
Readings:
- Reading 1 - Salganik, M. J., & Watts, D. J. (2009). Web-based experiments for the study of collective social dynamics in cultural markets. Topics in Cognitive Science, 1(3), 439-468.
- Reading 2 -
- Social Media Echo Chambers and Our Own Confirmation Bias. access here
- Bail et al (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216-9221.
- Dubois, E., & Blank, G. (2018). The echo chamber is overstated: the moderating effect of political interest and diverse media. Information, Communication & Society, 21(5), 729-745.
- Rosner, L., & Krmer, N. C. (2016). Verbal venting in the social web: Effects of anonymity and group norms on aggressive language use in online comments. Social Media+ Society, 2(3), 2056305116664220.
SYLLABUS:
| # | Date | Topic | Details |
|---|---|---|---|
| 1 | 2018-08-30 | Introduction and overview Social Software | What social computing is What we learn in this class Blogging and microblogging software: what are blogs? Who blogs? |
| 2 | 2018-09-06 | Social Networks and Social Network Analysis | Networks: definition, metrics Modeling and visualization Practical applications Social networks: Design, Technology, Features, and Impacts Project ideas posted |
| 3 | 2018-09-13 | Distributed collaboration | Wikis and Wikipedia Computer supported collaboration tools Content sharing Open source software development Quiz 1: Social network analysis |
| 4 | 2018-09-20 | Social information processing | Tagging Social navigation Social search Social bots Reading 1 Social software analsysis assignment posted |
| 5 | 2018-09-27 | No class | Instructor out of town Project proposal due |
| 6 | 2018-10-04 | Recommender systems | content based collaborative filtering chalenges of social information processing |
| 7 | 2018-10-11 | Evaluation methodologies and research ethics | Data collection Data analysis Usability studies Conducting research on the Internet Privacy IRB Quiz 2: Recommender systems |