CS 395C Social Media Behavior Spring 2023 University of Vermont (UVM) === Instructor: Dr. Jeremiah Onaolapo Prereqs: Knowledge of graph theory and computer programming (preferred language: Python). Graduate standing or advanced undergrad with computing-related background (e.g., CS 224). Note: This tentative syllabus is subject to change. --- Undergrads: Advanced undergrads with a strong computing background are welcome in this course. Feel free to email the instructor to request an override. --- Override Requests: In your email, describe your background in computing and how it has prepared you for this course. List and describe the relevant courses that you have taken in the past, and the relevant skills that you gained from them. Also include/mention your UVM NetID in the request. --- Course Description === Online social networks (OSNs)--under the general umbrella of social computing systems--are almost indispensable in our daily lives. Reddit, Twitter, and Facebook are a few of the many OSNs that keep us connected to our peers. OSNs also entertain us and keep us informed about what is going on in the world. However, OSNs come with their own share of problems, and have been subject to a considerable amount of scrutiny in recent times. For instance, OSNs have been subject to new forms of security and safety vulnerabilities, some of which have caused harms to individuals, groups, and societies (e.g., attempts at the manipulation of elections via the abuse of OSNs). Other types of social computing systems, such as microblogging systems, social recommendation systems, and social reputation-driven systems, have also suffered a similar fate. In this course, we will examine the underlying nature/structure of social computing systems (especially via the mathematical notion of graphs). We will explore various issues that have plagued social computing systems over the years, and study multiple ways by which malicious actors have abused them. We will also explore the methods by which researchers investigate social computing systems--via an empirical lens. Coursework/assessment will incorporate the exploration and analysis of models and data drawn from real and synthetic social information systems. Group project work will be required. Course Learning Objectives === 1. Examine the underlying nature/structure of social computing systems (especially via the mathematical notion of graphs). 2. Explore various issues that have plagued social computing systems over the years--and countermeasures. 3. Explore the methods by which researchers investigate social computing systems--via an empirical lens. Teaching Style === * Seminar. Section Expectations === We will study and discuss research papers on social computing systems. Students should expect to spend 6-8 hours a week on coursework outside of class, with additional time for the semester-long group project. Coursework/assessment will incorporate the exploration and analysis of models and data drawn from real and synthetic social information systems. Group project work will be required. Students should also expect to lead some discussions and deliver presentations in class. Evaluation === * Grades are based on: Attendance and participation, in-class discussions of research papers, written paper reviews (weekly), and a group project. Note: There is no exam/final exam in this course. Required Course Materials - Research Papers === * A pool of research papers will be announced at the beginning of the Spring 2023 semester. All of those papers are publicly available (for free). Required Course Materials - Textbooks === * None. You do not have to purchase any textbook for this course. Recommended Course Materials - Textbook === The recommended textbook is the following (available online, free): * Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press. Tentative Schedule of Topics (Spring 2023) === Note: This list is subject to change. * Social Graphs * Interaction Graphs * Privacy Issues * Identity Theft and Phishing * Social Reputation (e.g., PageRank) * Social Bots * Social Spam * Social Malware * Malicious Crowdsourcing (Crowdturfing) * Fake/Compromised Social Accounts * Hate Speech * Disinformation and Misinformation * Affective Polarization and Echo Chambers * Data Ethics * Information Flow [NB: Last updated Nov. 21, 2022]