Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks

Hongzhan LIN, Jing Ma*, Mingfei Cheng, Zhiwei Yang, Liangliang Chen, Guang Chen*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contribution

Abstract

Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to the strict relation of user responses or oversimplify the conversation structure. In this study, to substantially reinforces the interaction of user opinions while alleviating the negative impact imposed by irrelevant posts, we first represent the conversation thread as an undirected interaction graph. We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and attends over the posts that can semantically infer the target claim. Extensive experiments on three Twitter datasets demonstrate that our rumor detection method achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.
Original languageEnglish
Title of host publicationProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics (ACL)
Pages10035-10047
Number of pages13
DOIs
Publication statusPublished - Nov 2021
Event2021 Conference on Empirical Methods in Natural Language Processing - Barceló Bávaro Convention Centre, and also Virtually, Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021
https://2021.emnlp.org/

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2021
Country/TerritoryDominican Republic
CityPunta Cana
Period7/11/2111/11/21
Internet address

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