Cross-document Misinformation Detection based on Event Graph Reasoning

Xueqing Wu, Kung-Hsiang Huang, Yi Fung, Heng Ji


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.
Anthology ID:
2022.naacl-main.40
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
543–558
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2022.naacl-main.40/
DOI:
10.18653/v1/2022.naacl-main.40
Bibkey:
Cite (ACL):
Xueqing Wu, Kung-Hsiang Huang, Yi Fung, and Heng Ji. 2022. Cross-document Misinformation Detection based on Event Graph Reasoning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 543–558, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Cross-document Misinformation Detection based on Event Graph Reasoning (Wu et al., NAACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2022.naacl-main.40.pdf
Video:
 https://preview.aclanthology.org/build-pipeline-with-new-library/2022.naacl-main.40.mp4
Code
 shirley-wu/cross-doc-misinfo-detection
Data
RealNews