Cross-document misinformation detection based on event graph reasoning
Wu, Xueqing
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https://hdl.handle.net/2142/120247
Description
Title
Cross-document misinformation detection based on event graph reasoning
Author(s)
Wu, Xueqing
Issue Date
2023-04-12
Director of Research (if dissertation) or Advisor (if thesis)
Ji, Heng
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Natural Language Processing
Misinformation Detection
Cross-document Analysis
Event Coreference Resolution
Graph Neural Networks
Language
eng
Abstract
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.
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