Date of Award

5-16-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Nitin Agarwal

Abstract

Social media technology has disrupted and revolutionized citizen participation in protests, demonstrations, and social justice movements. In recent years, online information campaigns have moved from the more formal and organizationally backed collective action, to new forms of citizen based and personalized organization known as connective action. Social media platforms also provide a method to coordinate and influence behavior. These platforms can become catalysts for deviant behavior that results in dissemination of misinformation, disinformation, and mobilization of physical crowds. In this context, there is a need to understand the behaviors that motivate the actors and groups that participate in these social media movements. A major impediment to quickly understanding the behaviors that drive these cyber-crowd movements is that they often require interviews and surveys of the participants to extract detailed information about who is at the center of organizing these groups, what identities the group associates itself with, and how or why people were drawn in to participate. Computational analysis as a complement to existing survey methods offers in-depth insights about the role of identity in online movements. The use of computational methods to study connective action overall is an understudied area that can also provide valuable insights into the behavior and emergence of these movements. The research in this dissertation provides a multi-method, interdisciplinary approach to analyze connective action behavior using computational social science.

Download

Included in

Social Media Commons

Share

COinS