Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection

Jiawen Li, Shiwen Ni, Hung Yu Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Existing rumor detection strategies typically provide detection labels while ignoring their explanation. Nonetheless, providing pieces of evidence to explain why a suspicious tweet is rumor is essential. As such, a novel model, LOSIRD, was proposed in this paper. First, LOSIRD mines appropriate evidence sentences and classifies them by automatically checking the veracity of the relationship of the given claim and its evidence from about 5 million Wikipedia documents. LOSIRD then automatically constructs two heterogeneous graph objects to simulate the propagation layout of the tweets and code the relationship of evidence. Finally, a graphSAGE processing component is used in LOSIRD to provide the label and evidence. To the best of our knowledge, we are the first one who combines objective facts and subjective views to verify rumor. The experimental results on two real-world Twitter datasets showed that our model exhibited the best performance in the early rumor detection task and its rumor detection performance outperformed other baseline and state-of-the-art models. Moreover, we confirmed that both objective information and subjective information are fundamental clues for rumor detection.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationACL-IJCNLP 2021
EditorsChengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
PublisherAssociation for Computational Linguistics (ACL)
Pages705-715
Number of pages11
ISBN (Electronic)9781954085541
Publication statusPublished - 2021
EventFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online
Duration: 2021 Aug 12021 Aug 6

Publication series

NameFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Conference

ConferenceFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021
CityVirtual, Online
Period21-08-0121-08-06

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

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