Distilling arguments: A study of human and LLM persuasion in the online discourse
Gurjar, Omkar
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https://hdl.handle.net/2142/129251
Description
Title
Distilling arguments: A study of human and LLM persuasion in the online discourse
Author(s)
Gurjar, Omkar
Issue Date
2025-04-25
Director of Research (if dissertation) or Advisor (if thesis)
Chandrasekharan, Eshwar
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Large Language Models
Online Persuasion
Argument Summarization
Language
eng
Abstract
Persuasion is a key component of human interaction and has been extensively studied by computer scientists. Recent breakthroughs in AI agents, particularly large language models (LLMs), have opened up unprecedented avenues of online interaction, where users are increasingly exposed to AI-generated content. This makes it imperative to study the dynamics of persuasion in online communities and assess the effects LLMs might have on them. In this thesis, we address three crucial areas. First, we examine online user debates and characterize the persuasive strategies employed across different topics. Next, we evaluate the ability of state-of-the-art LLMs to generate and detect persuasive content. Finally, we address the potential misuse of LLMs towards influencing public opinion and explore summarization-based mitigation strategies. Our findings show that humans’ persuasive strategies vary significantly across topics, and LLMs demonstrate a reasonable understanding of persuasive content. Further, we find that LLMs tend to emphasize factual elements when summarizing arguments, although the results differ highly with the topic. We believe our work offers valuable insights into the dynamics of online persuasion and contributes to building robust guardrails against AI-generated persuasive content.
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