Description: This course is geared toward developing a broad understanding of the characteristics of today’s online social and crowd systems, including the opportunities and challenges that engender this emergent area. We will focus on the study of different social processes, behavior, and context on today's online social platforms, and learn how to make sense of the vast repositories of data that are generated on these platforms everyday. We will also learn about the design principles behind these systems and the key issues that arise from the widespread adoption of social computing systems in the wild.

The course will assign weekly readings on a variety of topics (see topics and schedule table), and students will be required to participate in a group term project. There will be a midterm exam (20%) and two assignments which will involve mini individual projects. Students will also be required to write a critical review on one assigned paper every week.

The term project will be 4-person group projects. Each student will need to clearly articulate their concrete contribution in the group project. Topic of the project can be picked by the student groups after discussion with the instructor; the instructor will also provide a set of sample project ideas in class materials. If the project requires data analysis, a contribution of the project could be collecting that data, or the students could also use any of the publicly available social datasets available online. Each project will require both original work as well as a small number of compulsory analyses that cover key concepts from the course.

Prerequisites: In terms of prerequisite skills, students need to have basic knowledge of statistics, preliminary machine learning, and a willingness to do interdisciplinary research.. An overview of the concepts and tools needed will be reviewed across the semester, however in-depth coverage of the fundamentals is not in the scope of this course. This is NOT a machine learning or data mining course. Students also need to be proficient in programming, in an object-oriented/scripting language (e.g., Python, Perl, Java, C#). Experience in use of a scientific computing software like R and Matlab is a bonus, but not required. Students should be prepared to apply what they have learned in prior computer science courses to this emerging new field. You are expected to quickly learn many new things. For example, your project may require you to fetch Twitter data using the Twitter API or analyze posts from Reddit using pre-existing libraries (like python nltk), which should not be too challenging if you already know high-level languages like Python. Please make sure you are comfortable with this.

Course Objectives:
The objectives of this course are:

• Collection and analysis of large-scale social data.
• Exploration of a variety of quantitative methodologies that could be applied to the study of social computing systems.
• Designing and building social tools that augment current social computing systems.
• Apply social data to answer questions in a variety of practical scenarios and domains, such as politics and health.
• Above all, understand the research issues, challenges, and opportunities in this field.

Grading:
(1) Term Project (35%) - One semester-long term project in a group of 4 students.
(2) Homework (18%) - There will be two homework assignements.
(3) Paper presentation (10%) - One 12 minutes presentation individually presented by each student.
(4) Midterm (20%) - One open book midterm exam based on the topics discussed in the class.
(5) Reading Reflection (12%) - One reading reflection due every week.
(6) Participation (5%) - In class discussion and online participation through Piazza.

- No final examination.
- The final project can be implemented with any platform or programming language.

Textbook: N/A
Reference books:
(1) Writing for Social Scientists, Howard Becker
(2) The Elements of Style, Strunk & White
(3) Networks, Crowds, and Markets, David Easley and Jon Kleinberg
(4) Six Degrees, Duncan Watts
(5) On Individuality and Social Forms, Georg Simmel
(6) Networked, Barry Wellman
(7) Machine Learning for Hackers, Drew Conway and John Myles White
(8) Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper
(9) The design of Everyday Things, Don Norman


Late Policy: Assignments must be submitted electronically to Blackboard by the assigned time on the due date. The timestamp on Blackboard will be used to evaluate lateness. Assignments submitted after the assigned time will be considered late and will be penalized as follows:

• Reading Reflections: No late days. All reading responses are due at 1pm on the day of class. Responses to readings serve to stir class discussions, hence there is no point if you submit it at a later time after class.
• In-class paper presentation: No late days. All materials due at 1pm on the day of your presentation.
• Midterm project report: Late days allowed. However, late assignments will be penalized at a rate of one grade step (e.g., A becomes A-) per day. Submissions more than five days late will not be accepted.
• Final project presentation: No late days.
• Final project report: No late days. Due on the day of final exam.

Extensions for well justified reasons may be made by written request, well in advance of the deadline.
Typically, such extensions are only granted once per student per semester, and only up to one week. Extensions are not automatic. Last-minute requests, or requests for extensions at or after the deadline, will be denied other than in the most extenuating circumstances. Written documentation of extenuating circumstances (serious illness, death in the family) will be required.