Content Moderation in Social Media: The Steel Armor of Social Media

Zoom link for remote participants

Teaching Faculty Candidate Seminar

Abstract: To counteract the spread of abuse and misinformation, social media platforms have established and employed content moderation. In this talk, I will present various aspects of content moderation, including their coverage, fairness, and effectiveness. This research is a data-driven and it involves both social and technical aspects. First, I provide a systematization of knowledge on social media content moderation, both in research and practice. Additionally, I will discuss my proposed novel frameworks to detect previously undetected misinformation about technological tools, as well as to audit the fairness of black-box algorithms used for content moderation. Lastly, I will discuss my proposed methods for examining the short and long-term effects of content moderation on the platform’s content and users’ discourse. To showcase my teaching objective to nurture critical thinking and ensure that my students are always supported while maintaining awareness that, for some of the students, this is their first security class, I will review user authentication methods and the various challenges.

Bio: Mohit Singhal is a Ph.D. candidate in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), advised by Prof. Shirin Nilizadeh. His research interests span content moderation of social media, security and privacy, fairness of artificial intelligence systems, data mining, and misinformation detection. His research has appeared in ICWSM and Euro S&P. His research on content moderation has been cited in The US Supreme Court and European Union Parliament and he was the recipient of “Best Technical Poster” in NDSS 2022. He is currently the instructor of the Information Security course. He is the recipient of the “Best Teaching Assistant Award” in 2022 at UTA.