DESCRIPTION:
This course focuses on how social groups form and evolve, how membeOArs 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.
COURSE FORMAT:
To address the pandemic circumstance, this course is designed following the flipped classroom model; i.e. lectures are going to be recorded and available online for you to watch. Every week we will meet for 1 hour during the class time for dicussion of concepts in the lectures and interaction around activites, assignments, and the project. The recorded lectures will be broken down into smaller topics to reduce the size of the recording to not more than 15 minutes. You can attend the synchronous component of the class online but there is also a physical room dedicated to this class for those who would like to be present with other students. As the instructor, I will be joining the class online through zoom. Lecture recordings include activities that will be graded and are considered the in-class activities portion of your grade.
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-videos activities [15 points]: the activities will be embedded in recorded lecture materials
- 3 Quizzes [15 points]: Social network analysis, social capital, Recommender systems
- Readings and reading summaries [20 points]: this is an individual assignment and involves two reading topics. Each reading assignment will include reading at least two papers, synthesize them and provide a summary of the papers as a short presentation
- Social data analysis assignment [15 points]: this is an anindividual analysis and involves conducting analysis on a data extracted from a social computing system to explore and understand a problem of interest.
- Final project [35 points]
OFFICE HOUR:
Email or online meetings by appointment:
- Instructor email: rfarzan at pitt dot edu
- TA email (Nuray Baltaci): NUB2 at pitt dot edu
TOPICS TO BE COVERED:
- Social software
- Social computing technologies
- Social information processing
- Human computation and collective intelligence
- Social capital
- Design of Online communities: Socialization of newcomers; Encouraging participation
- Social Computing Research Methodologies
- Visualization and sense making
- Social computing and crisis
- Social computing ethics
Readings:
Reading 1: Social software - individual and social impact
- Yu, R. P., Ellison, N. B., & Lampe, C. (2018). Facebook use and its role in shaping access to social benefits among older adults. Journal of Broadcasting & Electronic Media, 62(1), 71-90. [PDF]
- Enikolopov, R., Petrova, M., & Zhuravskaya, E. (2019). Political Effects of the Internet and Social Media. [PDF]
Reading 2: collective intelligence
- Hecht, B., & Terveen, L. (2017). The Role of Human Geography in Collective Intelligence. Collective Intelligence. [PDF]
- Arif, A., Robinson, J. J., Stanek, S. A., Fichet, E. S., Townsend, P., Worku, Z., & Starbird, K. (2017, February). A closer look at the self-correcting crowd: Examining corrections in online rumors. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing (pp. 155-168). [PDF]
SYLLABUS:
| # | Date | Topic | Details | Recordings |
|---|---|---|---|---|
| 1 | 2020-08-19 | Introduction and overview | Course logistics and requirements Overview of what social computing is about and what you will learn in this course | Part 1Part 2Part 3Part 4 |
| 2 | 2020-08-26 | Social software | What is social software? What are examples of social software? What should we know about social software | Part 1Part 2Part 3Part 4 |
| 3 | 2020-09-02 | Distributed collaboration | Computer supported collaboration tools Content sharing Open source software development Project ideas due | Part 1Part 2Part 3Part 4Part 5 |
| 4 | 2020-09-09 | Social Networks and Social Network Analysis | Networks: definition, metrics Social networks: Design, Technology, Features, and Impacts Social networks: Why and How Reading 1 due | Part 1Part 2Part 3Part 4Part 5 |
| 5 | 2020-09-16 | Social computing research methods and research ethics | Data collection Data analysis Usability studies Conducting research on the Internet Privacy IRB Project proposal due | Part 1Part 2Part 3Part 4Part 5Part 6 |
| 6 | 2020-09-23 | Analysis of social data | Collecting social traces: How and Why Wikipedia APIs Quiz 1: Social network analysis | Part 1Part 2Part 3 |
| 7 | 2020-09-30 | Social data analysis | Visualization Sense-making | |
| 8 | 2020-10-07 | Project progress report | In-class presentation of developed project idea, progress report, and feedback on your projects | |
| 9 | 2020-10-14 | Social capital | Definitions and measures Social capital and social networks Role of online communities on social capital Social data analysis assignment DUE | |
| 10 | 2020-10-21 | Designing online communities: challenges and supporting strategies | Dealing with newcomers under-contribution problem Encouraging contributions to online communities Strategies supported by social science theories Quiz 2: Social capital | |
| 11 | 2020-10-28 | Human computation and collective intelligence | Crowdsourcing Mechanical turk Purposeful games Creative crowdsourcing Ethical issues of crowdsourcing Reading 2 due | |
| 12 | 2020-11-04 | Recommender systems | content based collaborative filtering chalenges of social information processing | |
| 13 | 2020-11-11 | Social information processing | Tagging Social navigation Social search Social bots Quiz 3: Recommender systems | |
| 14 | 2020-11-18 | Final project | Final project presentations | |
| 15 | 2020-11-25 | Final report due | Submit final report online |
COURSE POLICIES
Health and Safety Statement: In the midst of this pandemic, it is extremely important that you abide by public health regulations and University of Pittsburgh health standards and guidelines. While in class, at a minimum this means that you must wear a face covering and comply with physical distancing requirements; other requirements may be added by the University during the semester. These rules have been developed to protect the health and safety of all community members. Failure to comply with these requirements will result in you not being permitted to attend class in person and could result in a Student Conduct violation. For the most up-to-date information and guidance, please visit coronavirus.pitt.edu and check your Pitt email for updates before each class.
Academic Integrity: You are expected to be fully aware of your responsibility to maintain a high quality of integrity in all of your work. All work must be your own, unless collaboration is specifically and explicitly permitted as in the course group project. Any unauthorized collaboration or copying will at minimum result in no credit for the affected assignment and may be subject to further action under the University Guidelines for Academic Integrity. You are expected to have read and understood these Guidelines. A document discussing these guidelines was included in your orientation materials.
Attendance: Class attendance, while not mandatory, is required if you want to succeed in this course, especially since the course does not have any course book and involves a lot of in-class discussions. If you have missed the lecture, make sure that you have a copy of the slides. All the lecture materials will be uploaded online. The class participation credit is engineered to encourage your attendance.
Late Submissions: Homework or projects submitted after due date will be accepted, but your objective grade will be scaled so that you lose 10% of the grade for every late working day. I.e., if you will submit your work one week late, you will lose 70% of the grade.
Concerning Students with Disabilities: If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union, (412) 648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.
An important note on plagiarism: Cheating and plagiarism will not be tolerated. Students caught cheating or plagiarizing will receive no credit for the assignment on which the cheating occurred. Additional actions -- including assigning the student a failing grade in the class or referring the case for disciplinary action -- may be taken at the discretion of the instructors. You may incorporate excerpts from publications by other authors, but they must be clearly marked as quotations and properly attributed. You may obtain copy editing assistance, and you may discuss your ideas with others, but all substantive writing and ideas must be your own or else be explicitly attributed to another, using a citation sufficiently detailed for someone else to easily locate your source.