{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120247"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120247","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Cross-document misinformation detection based on event graph reasoning","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Wu, Xueqing"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Ji, Heng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Natural Language Processing","Misinformation Detection","Cross-document Analysis","Event Coreference Resolution","Graph Neural Networks"],"languages":["en","eng"],"rights":["Copyright 2023 Xueqing Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120247","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ji, Heng"]},{"key":"dc:creator","label":"Author","values":["Wu, Xueqing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-12"]},{"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":["Natural Language Processing","Misinformation Detection","Cross-document Analysis","Event Coreference Resolution","Graph Neural Networks"]}]},{"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 2023 Xueqing Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120247"]}]},{"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 2023-09-01 without embargo terms","The student, Xueqing Wu, accepted the attached license on 2023-04-11 at 00:20.","The student, Xueqing Wu, submitted this Thesis for approval on 2023-04-11 at 00:31.","This Thesis was approved for publication on 2023-04-12 at 16:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18943 on 2023-09-01 at 17:08:10","For emerging events, human readers are often exposed to both real news and fake news. Multiple news articles may contain complementary or contradictory information that readers can leverage to help detect fake news. Inspired by this process, we propose a novel task of cross-document misinformation detection. Given a cluster of topically related news documents, we aim to detect misinformation at both document level and a more fine-grained level, event level. Due to the lack of data, we generate fake news by manipulating real news, and construct 3 new datasets with 422,276, and 1,413 clusters of topically related documents, respectively. We further propose a graph-based detector that constructs a cross-document knowledge graph using cross-document event coreference resolution and employs a heterogeneous graph neural network to conduct detection at two levels. We then feed the event-level detection results into the document-level detector. Experimental results show that our proposed method significantly outperforms existing methods by up to 7 F1 points on this new task. Codes and data are at https://github.com/shirley-wu/cross-doc-misinfo-detection."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Cross-document misinformation detection based on event graph reasoning"]}]}],"canonical_facts":{"dc:contributor":["Ji, Heng"],"dc:creator":["Wu, Xueqing"],"dc:date":["2023-05","2023-04-12"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Xueqing Wu, accepted the attached license on 2023-04-11 at 00:20.","The student, Xueqing Wu, submitted this Thesis for approval on 2023-04-11 at 00:31.","This Thesis was approved for publication on 2023-04-12 at 16:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18943 on 2023-09-01 at 17:08:10","For emerging events, human readers are often exposed to both real news and fake news. Multiple news articles may contain complementary or contradictory information that readers can leverage to help detect fake news. Inspired by this process, we propose a novel task of cross-document misinformation detection. Given a cluster of topically related news documents, we aim to detect misinformation at both document level and a more fine-grained level, event level. Due to the lack of data, we generate fake news by manipulating real news, and construct 3 new datasets with 422,276, and 1,413 clusters of topically related documents, respectively. We further propose a graph-based detector that constructs a cross-document knowledge graph using cross-document event coreference resolution and employs a heterogeneous graph neural network to conduct detection at two levels. We then feed the event-level detection results into the document-level detector. Experimental results show that our proposed method significantly outperforms existing methods by up to 7 F1 points on this new task. Codes and data are at https://github.com/shirley-wu/cross-doc-misinfo-detection."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120247"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Xueqing Wu"],"dc:subject":["Natural Language Processing","Misinformation Detection","Cross-document Analysis","Event Coreference Resolution","Graph Neural Networks"],"dc:title":["Cross-document misinformation detection based on event graph reasoning"],"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":"2026-07-22T22:24:57Z"}