{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129251"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129251","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Distilling arguments: A study of human and LLM persuasion in the online discourse","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Gurjar, Omkar"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chandrasekharan, Eshwar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-25","date_published":"2025-04-25","updated_at":"2026-07-22T22:25:04Z","subjects":["Large Language Models","Online Persuasion","Argument Summarization"],"languages":["en","eng"],"rights":["Copyright 2025 Omkar Gurjar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129251","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chandrasekharan, Eshwar"]},{"key":"dc:creator","label":"Author","values":["Gurjar, Omkar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-25","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Large Language Models","Online Persuasion","Argument Summarization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Omkar Gurjar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129251"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Omkar Gurjar, accepted the attached license on 2025-04-25 at 13:28.","The student, Omkar Gurjar, submitted this Thesis for approval on 2025-04-25 at 13:36.","This Thesis was approved for publication on 2025-04-25 at 16:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21972 on 2025-10-19 at 18:11:00","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Distilling arguments: A study of human and LLM persuasion in the online discourse"]}]}],"canonical_facts":{"dc:contributor":["Chandrasekharan, Eshwar"],"dc:creator":["Gurjar, Omkar"],"dc:date":["2025-04-25","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Omkar Gurjar, accepted the attached license on 2025-04-25 at 13:28.","The student, Omkar Gurjar, submitted this Thesis for approval on 2025-04-25 at 13:36.","This Thesis was approved for publication on 2025-04-25 at 16:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21972 on 2025-10-19 at 18:11:00","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129251"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Omkar Gurjar"],"dc:subject":["Large Language Models","Online Persuasion","Argument Summarization"],"dc:title":["Distilling arguments: A study of human and LLM persuasion in the online discourse"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}