{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/130019"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/130019","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Analysis of hierarchically polarized communities on social media","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","abstract_has_math":false,"creators":["Sun, Dachun"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Abdelzaher, Tarek","Tong, Hanghang","Ji, Heng","Lebiere, Christian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-08","date_published":"2025-07-08","updated_at":"2026-07-22T22:25:06Z","subjects":["Social Network Analysis","Polarization Identification","Community Response Generation"],"languages":["en","eng"],"rights":["Copyright 2025 Dachun Sun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/130019","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abdelzaher, Tarek","Tong, Hanghang","Ji, Heng","Lebiere, Christian"]},{"key":"dc:creator","label":"Author","values":["Sun, Dachun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-08","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Social Network Analysis","Polarization Identification","Community Response Generation"]}]},{"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 Dachun Sun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/130019"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","The student, Dachun Sun, accepted the attached license on 2025-07-01 at 17:04.","The student, Dachun Sun, submitted this Dissertation for approval on 2025-07-01 at 17:11.","This Dissertation was approved for publication on 2025-07-08 at 09:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22395 on 2025-10-21 at 10:05:37","Social media platforms have profoundly transformed public discourse, providing a global forum for individuals to share information, express opinions, and build communities. While they foster unprecedented connectivity, they also exacerbate polarization, forming communities characterized by hierarchical ideological divisions. Many existing computational approaches oversimplify these dynamics, reducing polarization to binary oppositions and neglecting the nuanced, evolving structure of beliefs within online networks. Furthermore, there has been limited exploration into predicting community responses to new hypothetical posts, leaving critical gaps in the understanding of and strategies for mitigating polarization. This dissertation addresses three central research questions: How can hierarchically polarized communities be identified and characterized on social media platforms? How can the collective responses of these communities to new social stimuli be realistically simulated, capturing their structural complexity and the cognitive mechanisms that drive opinion formation? And how can the discovered solutions be implemented in a practical tool? To answer these questions, a suite of computational techniques is developed to detect, represent, and simulate complex community structures in social media environments. A dynamic polarized belief representation framework grounded in recurrent graph autoencoders is introduced, allowing for the tracking and analysis of hierarchical community structures as they evolve over time. To address the scarcity of labeled data, a perturbation-based active learning strategy is proposed to optimize label efficiency in semi-supervised settings by strategically selecting and labeling informative nodes within the social graph. This dissertation also advances the state of the art in community response generation by developing a retrieval-augmented generation (RAG) framework that leverages both historical social media responses and external knowledge to forecast community reactions to hypothetical posts. To better reflect human biases, cognitive mechanisms such as memory recency, frequency, and similarity weighting are incorporated, emulating human biases in opinion dynamics and bridging the gap between rational computation and real-world behavior. Much of this work is integrated into an analytics tool for conflict monitoring and intervention, validated through demonstrations on real-world social media datasets. Overall, this dissertation explores these core issues related to identifying, understanding, and simulating the evolution and responses of hierarchical and dynamic polarized communities on social media, thereby supporting efforts to address the challenges of polarization and radicalization in online environments."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Analysis of hierarchically polarized communities on social media"]}]}],"canonical_facts":{"dc:contributor":["Abdelzaher, Tarek","Tong, Hanghang","Ji, Heng","Lebiere, Christian"],"dc:creator":["Sun, Dachun"],"dc:date":["2025-07-08","2025-08"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","The student, Dachun Sun, accepted the attached license on 2025-07-01 at 17:04.","The student, Dachun Sun, submitted this Dissertation for approval on 2025-07-01 at 17:11.","This Dissertation was approved for publication on 2025-07-08 at 09:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22395 on 2025-10-21 at 10:05:37","Social media platforms have profoundly transformed public discourse, providing a global forum for individuals to share information, express opinions, and build communities. While they foster unprecedented connectivity, they also exacerbate polarization, forming communities characterized by hierarchical ideological divisions. Many existing computational approaches oversimplify these dynamics, reducing polarization to binary oppositions and neglecting the nuanced, evolving structure of beliefs within online networks. Furthermore, there has been limited exploration into predicting community responses to new hypothetical posts, leaving critical gaps in the understanding of and strategies for mitigating polarization. This dissertation addresses three central research questions: How can hierarchically polarized communities be identified and characterized on social media platforms? How can the collective responses of these communities to new social stimuli be realistically simulated, capturing their structural complexity and the cognitive mechanisms that drive opinion formation? And how can the discovered solutions be implemented in a practical tool? To answer these questions, a suite of computational techniques is developed to detect, represent, and simulate complex community structures in social media environments. A dynamic polarized belief representation framework grounded in recurrent graph autoencoders is introduced, allowing for the tracking and analysis of hierarchical community structures as they evolve over time. To address the scarcity of labeled data, a perturbation-based active learning strategy is proposed to optimize label efficiency in semi-supervised settings by strategically selecting and labeling informative nodes within the social graph. This dissertation also advances the state of the art in community response generation by developing a retrieval-augmented generation (RAG) framework that leverages both historical social media responses and external knowledge to forecast community reactions to hypothetical posts. To better reflect human biases, cognitive mechanisms such as memory recency, frequency, and similarity weighting are incorporated, emulating human biases in opinion dynamics and bridging the gap between rational computation and real-world behavior. Much of this work is integrated into an analytics tool for conflict monitoring and intervention, validated through demonstrations on real-world social media datasets. Overall, this dissertation explores these core issues related to identifying, understanding, and simulating the evolution and responses of hierarchical and dynamic polarized communities on social media, thereby supporting efforts to address the challenges of polarization and radicalization in online environments."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/130019"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Dachun Sun"],"dc:subject":["Social Network Analysis","Polarization Identification","Community Response Generation"],"dc:title":["Analysis of hierarchically polarized communities on social media"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}