Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom-Reference-Cited by-同舟云学术

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

Published:2024-05-13 Issue: Volume: Page:2452-2463
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Wang Bo¹^ORCID,Ma Jing²^ORCID,Lin Hongzhan²^ORCID,Yang Zhiwei³^ORCID,Yang Ruichao²^ORCID,Tian Yuan⁴^ORCID,Chang Yi⁴^ORCID

Affiliation:

1. School of Artificial Intelligence, Jilin University & Hong Kong Baptist University, Changchun, China

2. Hong Kong Baptist University, Hong Kong SAR, China

3. Guangdong Institute of Smart Education, Jinan University, Guangzhou, China

4. School of Artificial Intelligence, Jilin University, Changchun, China

Funder

the National Key Research and Development Program of China

the Science and Technology Development Program of Jilin Province

the National Natural Science Foundation of China

Hong Kong RGC ECS

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645471

Reference39 articles.

1. Where is Your Evidence: Improving Fact-checking by Justification Modeling

2. Jennifer Allen, Antonio A Arechar, Gordon Pennycook, and David G Rand. 2021. Scaling up fact-checking using the wisdom of crowds. Science advances, Vol. 7, 36 (2021), eabf4393.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Survey on the Use of Large Language Models (LLMs) in Fake News;Future Internet;2024-08-19

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