research-article

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

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Yi ChangAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 2452 - 2463

https://doi.org/10.1145/3589334.3645471

Published: 13 May 2024 Publication History

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.

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Cited By

Papageorgiou EChronis CVarlamis IHimeur Y(2024)A Survey on the Use of Large Language Models (LLMs) in Fake NewsFuture Internet10.3390/fi1608029816:8(298)Online publication date: 19-Aug-2024
https://doi.org/10.3390/fi16080298
Condran SSerra ESpezzano F(2024)The Veracity Problem: Detecting False Information and its Propagation on Online Social Media NetworksProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680265(5447-5450)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680265
Wang BLi XLi CFu BPei SWang SSerra ESpezzano F(2024)Why Misinformation is Created? Detecting them by Integrating Intent FeaturesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679799(2304-2314)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679799
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Index Terms

Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom
1. Information systems
  1. Information systems applications
    1. Multimedia information systems

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
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Hady W. Lauw
Singapore Management University

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Cited By

Papageorgiou EChronis CVarlamis IHimeur Y(2024)A Survey on the Use of Large Language Models (LLMs) in Fake NewsFuture Internet10.3390/fi1608029816:8(298)Online publication date: 19-Aug-2024
https://doi.org/10.3390/fi16080298
Condran SSerra ESpezzano F(2024)The Veracity Problem: Detecting False Information and its Propagation on Online Social Media NetworksProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680265(5447-5450)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680265
Wang BLi XLi CFu BPei SWang SSerra ESpezzano F(2024)Why Misinformation is Created? Detecting them by Integrating Intent FeaturesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679799(2304-2314)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679799
Nan QSheng QCao JHu BWang DLi JSerra ESpezzano F(2024)Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679519(1732-1742)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679519
Condran SBewong MKwashie SIslam MAltas ICondran J(2024)MAPX: An Explainable Model-Agnostic Framework for Detecting False Information on Social Media NetworksWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0567-5_10(116-127)Online publication date: 3-Dec-2024
https://doi.org/10.1007/978-981-96-0567-5_10

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