DESCRIPTION:
Social Computing is a field concerned with the intersection of technology and social interactions and social exchanges. This course focuses on how social groups are supported and augmented with technological systems as well as the mutual impact of technology on society and vice versa. The course is interdisciplinary, drawing from the fields of computer science, information science, and social psychology. 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:
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 about 90 minutes during the class time for discussion of concepts in the lectures and interaction around activities, assignments, and the project. The recorded lectures will be broken down into smaller topics to reduce the size of the recording to about 15 minutes. Lecture recordings will include activities that we will discuss during the class time. Lecture recordings will be posted by Sunday night before the Wednesday class, and you are expected to watch the lecture before the class on Wednesday
PREREQUISITES:
This course does not assume any 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 number of books and articles. References and inks to electronic copies are provided.
GRADING:
- Participation in in-class activities [15 points]: in-class activities are group activities. Each in-class activity will have a designated leader who will capture the group discussions and to report back to the class about the discussion. To get full participation grade, you should lead 8 to 10 activities depending on number of students in the class. Missing one or two classes should not affect your grade.
- 2 Quizzes [20 points]: Distributed collaboration, Social network analysis, social capital, Collective Intelligence, Recommender systems
- Reading assignments [15 points]: the reading assignment are designed as paired assignments. There are two papers assigned for each assignment. For each paper, one student will be designated as presenter and one student as the critic. Both students are required to read the papers carefully. The critic will come up with a set of 5 questions from the paper and the presenter prepares a presentation in response to those questions. During the class presentation, randomly selected team will present their questions and responses about the paper. The presenter is in charge of preparing the presentations and the critic is in charge of providing feedback on the presentation. The questions have to be conceptual and not too specific. Examples of good questions include: "What is the main problem the authors try to address in this paper?", "What approaches have the authors taken to address the problem?". Teams with best presentation will be awarded extra points
- Social data analysis assignment [15 points]: this is an individual assignment and requires you to analyze data from a social media site such as to study a social computing problem. The assignment will require you to use the social media API as well as visualization and network analysis.
- Final project [35 points]: The project involves design, prototyping, and evaluation of a social computing application related to a societal issues. It is a group project (groups of 3 or 4 students). Idea generation (5 points), Proposal (5 points), Mid-semester progress (10 points), Final presentation and Final report (15 points)
OFFICE HOUR:
Email or online meetings by appointment:
- Instructor email: rfarzan at pitt dot edu
- TA email:
TOPICS TO BE COVERED:
- Social software
- Virtual worlds and Metaverse
- Social networks and social network analysis
- Social data analysis
- Social information processing
- Human computation and collective intelligence
- Social computing theories
- Social computing ethics
SYLLABUS:
| # | Date | Topic | Details |
|---|---|---|---|
| 1 | 2023-01-11 | Introduction and overview | Course logistics and requirements Overview of what social computing is about and what you will learn in this course |
| 2 | 2023-01-18 | Social software | What is social software? Connecting people through technology Challenges of building social technology |
| 3 | 2023-01-25 | Virtual Worlds | Designing virtual worlds MUD Metaverse Virtual identity Reading presentation 1: in-class presentation |
| 4 | 2023-02-01 | Distributed collaboration | Computer supported collaboration tools Content sharing Open source software development Project ideas due |
| 5 | 2023-02-08 | Project topic and team formation | Instructor will provide feedback for each team on the general idea, methods, and plan of their project |
| 6 | 2023-02-15 | Social Networks and Social Network Analysis | Networks: definition, metrics Social networks: Design, Technology, Features, and Impacts Social networks analysis: Why and How |
| 7 | 2023-02-22 | Social data analysis | Visualization and sense-making of social data Social and collaborative exploration of data Quiz 1: Distributed collaboration and Social network analysis |
| 8 | 2023-03-01 | Analysis of social data | Collecting social traces: How and Why Social media APIs |
| 9 | 2023-03-08 | No class | Spring break |
| 10 | 2023-03-15 | Social computing theories | Theories of personal behavior Theories of social behavior Mass communication theories |
| 11 | 2023-03-22 | Project progress report | In-class presentation of developed project idea, progress report, and feedback on your projects |
| 12 | 2023-03-29 | Social capital | Definitions and measures Social capital and social networks Role of online communities on social capital |
| 13 | 2023-04-05 | Socially intelligent computing | Collective intelligence Content based recommender systems Collaborative filtering recommender systems Quiz 2: Social Computing Theories |
| 14 | 2023-04-12 | Social information processing | Social navigation Social search Social bots Social data analysis assignment due |
| 15 | 2023-04-19 | Social computing: ethics and societal impact | Impact on physical and psychological well-being Interplay of online and offline world Social media ethics Reading presentation 2: in-class presentation |
| 16 | 2023-04-26 | Final project | Final project presentations and report |
COURSE POLICIES
Academic Integrity: You are expected to be fully aware of your responsibility to maintain a high quality of integrity in all 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 in-class activities are designed to help you learn the course materials better.
Late Submissions: Every student has a total of 3 days late-submission quota to use on your assignments; i.e. you can submit one assignment 3 days late and you will be ok, or you can submit 3 assignment each 1 day late and you will be ok. Late submission cannot be accepted for assignments that require in-class presentation.
Grades: Once grades for every assignment are posted, you have 5 days to review your grades and contact the instructor for any issues with you grade. This applies to your final grade as well. Complains past this period will not be considered.
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.