{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125603"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125603","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Online conversations: A study of their toxicity","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Alkhabaz, Ridha Monir A."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sundaram, Hari"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-16","date_published":"2024-07-16","updated_at":"2026-07-22T22:25:02Z","subjects":["Toxicity","Online Conversations","Graph Neural Networks","Turn-taking Paths","Terminating Conversational Structures","Network Science"],"languages":["en","eng"],"rights":["Copyright 2024 Ridha Alkhabaz"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125603","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sundaram, Hari"]},{"key":"dc:creator","label":"Author","values":["Alkhabaz, Ridha Monir A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-16","2024-08"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Toxicity","Online Conversations","Graph Neural Networks","Turn-taking Paths","Terminating Conversational Structures","Network Science"]}]},{"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 2024 Ridha Alkhabaz"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125603"]}]},{"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 2025-02-04 without embargo terms","The student, Ridha Alkhabaz, accepted the attached license on 2024-07-10 at 05:28.","The student, Ridha Alkhabaz, submitted this Thesis for approval on 2024-07-10 at 05:50.","This Thesis was approved for publication on 2024-07-16 at 15:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21035 on 2025-02-04 at 21:04:51","Social media platforms are essential spaces for modern human communication. There is a dire need to make these spaces most welcoming and engaging to their participants. A potential threat to this need is the propagation of toxic content in online spaces. Hence, it becomes crucial for social media platforms to detect early signs of a toxic conversation. In this work, we tackle the problem of toxicity prediction by proposing a definition for conversational structures. This definition empowers us to provide a new framework for toxicity prediction. Thus, we examine more than 1.18 million X (made by 4.4 million users), formerly known as Twitter, threads to provide a few key insights about the current state of online conversations. Our results indicated that most of the X threads do not exhibit a conversational structure. Also, our newly defined structures are distributed differently than previously thought of online conversations. Additionally, our definitions give a meaningful sign for models to start predicting the future toxicity of online conversations. We also showcase that message-passing graph neural networks outperform state-of-the-art gradient-boosting trees for toxicity prediction. Most importantly, we find that once we observe the first two terminating conversational structures, we can predict the future toxicity of online thread with ≈ 88 % accuracy. We hope our findings will help social media platforms better curate content in their spaces and promote more conversations in online spaces."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Online conversations: A study of their toxicity"]}]}],"canonical_facts":{"dc:contributor":["Sundaram, Hari"],"dc:creator":["Alkhabaz, Ridha Monir A."],"dc:date":["2024-07-16","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Ridha Alkhabaz, accepted the attached license on 2024-07-10 at 05:28.","The student, Ridha Alkhabaz, submitted this Thesis for approval on 2024-07-10 at 05:50.","This Thesis was approved for publication on 2024-07-16 at 15:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21035 on 2025-02-04 at 21:04:51","Social media platforms are essential spaces for modern human communication. There is a dire need to make these spaces most welcoming and engaging to their participants. A potential threat to this need is the propagation of toxic content in online spaces. Hence, it becomes crucial for social media platforms to detect early signs of a toxic conversation. In this work, we tackle the problem of toxicity prediction by proposing a definition for conversational structures. This definition empowers us to provide a new framework for toxicity prediction. Thus, we examine more than 1.18 million X (made by 4.4 million users), formerly known as Twitter, threads to provide a few key insights about the current state of online conversations. Our results indicated that most of the X threads do not exhibit a conversational structure. Also, our newly defined structures are distributed differently than previously thought of online conversations. Additionally, our definitions give a meaningful sign for models to start predicting the future toxicity of online conversations. We also showcase that message-passing graph neural networks outperform state-of-the-art gradient-boosting trees for toxicity prediction. Most importantly, we find that once we observe the first two terminating conversational structures, we can predict the future toxicity of online thread with ≈ 88 % accuracy. We hope our findings will help social media platforms better curate content in their spaces and promote more conversations in online spaces."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125603"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Ridha Alkhabaz"],"dc:subject":["Toxicity","Online Conversations","Graph Neural Networks","Turn-taking Paths","Terminating Conversational Structures","Network Science"],"dc:title":["Online conversations: A study of their toxicity"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}