Select a calendar:
Filter October Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for October 29, 2024
-
PhD Dissertation Defense - Yizhou Zhang
Tue, Oct 29, 2024 @ 04:30 PM - 06:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Towards Combatting Coordinated Manipulation to Online Public Opinions on Social Media
Location: KAP 138
Date and Time: October 29th, 2024: 4:30pm - 6:00pm
Committee Members: Yan Liu (Chair), Jieyu Zhao, and Kimon Drakopoulos
Abstract: Over the recent years, public opinions and online credibility have been suffering from the manipulation of campaigns that control malicious accounts to document and spread misinformation with specific narratives such as Fake News and Conspiracies. Such campaigns, also known as misinformation campaigns, are increasingly threatening various areas related to public opinions and decisions, such as politics and public health. Such threats, prominent in highly scrutinized societal events like the U.S. Presidential Elections and the COVID-19 pandemic, have significantly undermined societal trust and public interests. My thesis will discuss how to exploit machine learning to discover knowledge and skills that are helpful for combating these aforementioned social manipulation. More specifically, my thesis will present my research attempts to apply machine learning algorithms in three directions: Manipulation Source Identification, Susceptible Population Recognition and Automated Authenticity Verification. To identify the online manipulation from misinformation campaigns, my collaborators and I developed a series of neural temporal point process models that can recognize patterns of coordinated manipulators with data-driven learning and domain knowledge. To recognize users that are susceptible to specific misinformation, we developed a counterfactual neural network that can estimate the causal effect of a piece of misinformation on an individual user or a group of population. To complete our target on automated authenticity verification, we make use of the advances of Large Language Models (LLM), which can serve for generating a clarification for misinformation and reference for true information. To achieve this goal, more work on developing robust prompting engineering strategies is conducted to prevent the LLM from being deceived by the misinformation when verifying the genuineness of given text.Location: Kaprielian Hall (KAP) - 138
Audiences: Everyone Is Invited
Contact: Yizhou Zhang
