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:

OFFICE HOUR:

Monday 3:00-4:00 pm, or by appointment, 709 Information Science Building (135 North Bellefield Avenue)

TOPICS TO BE COVERED:

Readings:

SYLLABUS:

#DateTopicDetails
12019-01-10Introduction and overviewCourse logistics and requirements
Overview of what social computing is about and what you will learn in this course
22019-01-17Social Networks and Social Network AnalysisNetworks: definition, metrics
Social networks: Design, Technology, Features, and Impacts
Project proposal assignment posted
32019-01-24Social softwareWhat is social software?
What are examples of social software?
What should we know about social software
Quiz 1: Social network analysis
reading1
42019-01-31Distributed collaborationWikis and Wikipedia
Computer supported collaboration tools
Content sharing
Open source software development
Project proposal due
Social software analsysis assignment posted
52019-02-07Social information processingTagging
Social navigation
Social search
Social bots
62019-02-14Recommender systemscontent based
collaborative filtering
chalenges of social information processing
72019-02-21Evaluation methodologies and research ethicsData collection
Data analysis
Usability studies
Conducting research on the Internet
Privacy
IRB
Quiz 2: Recommender systems
82019-02-28Social software presentations
Social software analysis aassignment DUE
92019-03-07Social data analysisVisualization
Sense-making
APIs
Facebook
Wikipedia
Twitter
Reading 2
Social data analysis assignment posted
102019-03-14Spring breakNo class
112019-03-21Project progress reportIn-class presentation of developed project idea, progress report, and timeline for finishing the project
122019-03-28Social capitalDefinitions and measures
Social capital and social networks
Role of online communities on social capital
Reading 3
132019-04-04Challenges of online communitiesDealing with newcomers
under-contribution problem
Encouraging contributions to online communities
Strategies supported by social science theories
Quiz 3: Social capital
142019-04-11Human computation and collective intelligenceCrowdsourcing
Mechanical turk
Purposeful games
Creative crowdsourcing
Ethical issues of crowdsourcing
Social data analysis assignment DUE
152019-04-18Final projectFinal project poster session

COURSE POLICIES

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.