DESCRIPTION:

Social Computing technologies have become very prominent in our society and have changed many aspects of our lives, the way we interact, we read news, socialize, or play games. This course is designed to provide you with understanding of social computing technologies, including design of such systems, and how we can study them to understand how the systems are used, how they impact their users and communities, and the society at a large. Social Computing is an interdisciplinary course, drawing from the fields of Computer Science, Information Science, Psychology, and Economics. Throughout the course, we will cover key theories and technologies of social computing. Broadly, social computing can be understood as (1) computer systems supporting social behavior and (2) socially intelligent computing carried out by groups of individuals. As student in this class, you 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. short lecture recordings (around 15 min) are going to be available online for you to watch before the class. During the class time, we will spend time for discussion of concepts in the lectures and interaction around activities, assignments, and the course project. You are expected to watch the video recordings before the class. The recordings will be about 60 minutes for each week.

PREREQUISITES:

(INFSCI 0410 or INFSCI 1044) and (INFSCI 0510 or INFSCI 0419 or INFSCI 0019) and (INFSCI 0610 or INFSCI 1070). Also since this is an upper-level course and a rather research-based course, it involves 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:

Email or online meetings by appointment:

SYLLABUS:

#DateTopicDetailsActivities
12020-09-01Introduction and overviewCourse logistics and requirements
Overview of what social computing is about and what you will learn in this course
Introduction activity
Discussion of the movie Social Dilemma
22020-09-08Social softwareWhat is social software?
What are examples of social software?
What should we know about social software
Connecting people through technology
Building blocks of social software
Social programming
Building a blogging software
Discussion: What makes Twitter successful
32020-09-15Distributed collaboration softwareComputer supported collaboration tools
Content sharing
Open source software development
Project observation notes
Survival task experiment
Introducing Wikipedia assignment
Create Wikipedia account and user page
42020-09-22Virtual WorldsMUDs
Cases studies in using virtual worlds to understand real world phenomena
Virtual worlds during the Covid-19 pandemic
Project interview questions
Virtual Ethnography
52020-09-29Design challengesParticipation online
Attention economy in social media
Attracting new members
Retention of established members
Wikipedia assignment: Evaluate Wikipedia
designing a social media campaign
62020-10-06Project Presentation of proposals
Initial project proposal
72020-10-13Social Network AnalysisNetworks: definition, metrics
Why and when to do network analysis
Social network analysis tools
Data Analysis Assignment Part 1
Practice with network visualization tool
Discussion: Facebook degree of separation experiment
82020-10-20Social data analysisData gather with public APIs
Twitter API
Wikipedia API
Social Network Aanalysis Quiz
practice with use of APIs
Wikipedia data analysis
92020-10-27Social data visualization and sense makingData visualization
Location based visualization
Effective visualization of social data
Midterm design prototype
Practice with QGIS tool and visualization of local data
Play with NameVoyager: what is interesting about this application as social data visualization
102020-11-03Socially Intelligent ComputingCollective Intelligence
Crowdsourcing
Citizen Science
Human-Robot collaboration
Data Analysis Assignment Part 2
M&M activity
MTurk activity
Discussion: Why do ESP games work
112020-11-10Recommender SystemsContent-based recommendation
Collaborative Filtering
Data Analysis Assignment Part 3
Build a recommender system
Discussion: challenges of recommender systems
122020-11-17Social information processingSocial navigation
Social search
Recommender Systems Quiz
Social navigation vs. general navigation
Design a social navigation system for Wikipedia
132020-11-24No classThanksgiving break
142020-12-01Ethical issues of social computingData biases
Data collection
Online representation
Final presentation of Wikipedia work
Wikipedia presentation
Trivia game
152020-12-08Final projectProject poster session

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.