Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom

Bo Wang, Jing Ma*, Hongzhan Lin, Zhiwei Yang, Ruichao Yang, Yuan Tian*, Yi Chang*

*Corresponding author for this work

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

Abstract

Most fake news detection methods learn latent feature representations based on neural networks, which makes them black boxes to classify a piece of news without giving any justification. Existing explainable systems generate veracity justifications from investigative journalism, which suffer from debunking delayed and low efficiency. Recent studies simply assume that the justification is equivalent to the majority opinions expressed in the wisdom of crowds. However, the opinions typically contain some inaccurate or biased information since the wisdom of crowds is uncensored. To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework. Specifically, we first propose an evidence extraction module to split the wisdom of crowds into two competing parties and respectively detect salient evidences. To gain concise insights from evidences, we then design a prompt-based module that utilizes a large language model to generate justifications by inferring reasons towards two possible veracities. Finally, we propose a defense-based inference module to determine veracity via modeling the defense among these justifications. Extensive experiments conducted on two real-world benchmarks demonstrate that our proposed method outperforms state-of-the-art baselines in terms of fake news detection and provides high-quality justifications.

Original languageEnglish
Title of host publicationWWW '24: Proceedings of the ACM on Web Conference 2024
PublisherAssociation for Computing Machinery (ACM)
Pages2452-2463
Number of pages12
ISBN (Electronic)9798400701719
DOIs
Publication statusPublished - 13 May 2024
Event33rd ACM Web Conference, WWW 2024 - , Singapore
Duration: 13 May 202417 May 2024
https://dl.acm.org/doi/proceedings/10.1145/3589334
https://dl.acm.org/doi/proceedings/10.1145/3589335

Publication series

NameWWW 2024 - Proceedings of the ACM Web Conference

Conference

Conference33rd ACM Web Conference, WWW 2024
Country/TerritorySingapore
Period13/05/2417/05/24
Internet address

Scopus Subject Areas

  • Computer Networks and Communications
  • Software

User-Defined Keywords

  • competition in wisdom
  • defense-based inference
  • explainable
  • fake news detection
  • large language model

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