PhD Thesis Proposal - Siyi Guo

Title: Understanding Population Heterogeneities through Dynamic Behaviors    
 
Committee: Kristina Lerman (Chair), Fred Morstatter, Urbashi Mitra, Shanghua Teng    
 
Location: SAL 322     
 
Date and Time: Weds., May 29th: 12:00p - 1:30p   
 
Abstract:    
The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using these data to understand social behavior is difficult due to the long-tailed distributions of both contents and user attributes and the heterogeneity of topics and events discussed in the highly dynamic online environment. Existing methods typically rely on specific features like text content, activity patterns, or platform metadata, failing to holistically model user behavior across different modalities. To address these challenges, we aim to discover and model population heterogeneities by studying user behavioral dynamics on social media. First, we present a method for systematically detecting and measuring emotional reactions to offline events, and use it to uncover the different emotional reactions in US liberal and conservative populations to the overturn of Roe v. Wade. In the second part, we further model the heterogeneous user behaviors by a novel social media user representation learning framework, and demonstrate its versatility through two applications: 1) Measuring increased polarization in online discussions after major events by quantifying how users with different beliefs moved farther apart in the embedding space, and (2) Identifying inauthentic accounts involved in coordinated influence operations by detecting users posting similar content simultaneously. Our ability to discover and model user heterogeneity enables new solutions to important problems around disinformation, societal tensions, and online behavior understanding.
 
Zoom: https://usc.zoom.us/my/siyiguo

Location: Henry Salvatori Computer Science Center (SAL) - 322

Audiences: Everyone Is Invited

Contact: Siyi Guo

Event Link: https://usc.zoom.us/my/siyiguo