Mon, Apr 17, 2023 @ 01:00 PM - 02:30 PM
Thomas Lord Department of Computer Science
PHD Thesis Proposal: Julie Jiang
Committee: Emilio Ferrara (Chair), Barath Raghavan, Su Jung Kim, Jesse Thomason, Kristina Lerman
Title: Socially-infused Content Mining of Online Human Behavior
The vast amount of data generated by human behavior online provides valuable insight into how people interact with one another and with digital environments. However, mining this data can be time-consuming and computationally intensive. This dissertation proposes a unified language and network model that leverages the concept of homophily to efficiently analyze large-scale human behavior. By identifying patterns in network interactions and linguistic styles, this model can characterize political polarization, detect hateful and toxic users, and quantify users based on their moral foundation leanings. The findings demonstrate how seemingly simple patterns in online behavior can offer a deeper understanding of human behavior in digital environments. I apply this model to a range of real-world problems, including characterizing political polarization, understanding social influence on networks of hateful users, and contextualizing user behavior based on their moral foundation leanings. The findings demonstrate how seemingly simple patterns in online behavior can offer a deeper understanding of human behavior in digital environments.
Audiences: Everyone Is Invited
Contact: Asiroh Cham