PiercingEye: Identifying Both Faint and Distinct Clues for Explainable Fake News Detection with Progressive Dynamic Graph Mining

Yasan Ding, Bin Guo, Yan Liu, Hao Wang, Haocheng Shen, Zhiwen Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Explainability is crucial for the successful use of AI for fake news detection (FND). Researchers aim to improve the explainability of FND by highlighting important descriptions in crowd-contributed comments as clues. From the perspective of law and sociology, there are distinct clues that are easy to discover and understand, and faint clues that require careful observation and analysis. For example, in fake news related to COVID-Omicron showing increased pathogenicity and transmissibility, distinct clues might involve virologists' opinions regarding the inverse correlation between pathogenicity and transmissibility. Meanwhile, faint clues might be reflected in an infected person's claim that the symptoms are milder than a cold (indirectly indicating reduced pathogenicity). Occasionally, the statements of some ordinary eyewitnesses can decisively reveal the truth of the news, leading to the judgment of fake news. Existing methods generally use static networks to model the entire news life-cycle, which makes it fail to capture the subtle dynamic interactions between individual clues and news. Thereby faint clues, whose relations to the truth of news are challenging to be characterized and extracted directly, are more likely to be overshadowed by distinct clues. To address this issue, we propose an explainable FND method, dubbed as PiercingEye, which leverages dynamic interaction information to progressively mine valuable clues. PiercingEye models the news propagation topology as a dynamic graph, with interactive comments serving as nodes, and employs the time-semantic encoding mechanism to refine the modeling of temporal interaction information between comments and news to preserve faint clues. Subsequently, it utilizes the self-attention mechanism to aggregate distinct and faint clues for FND. Experimental results demonstrate that PiercingEye outperforms state-of-the-art methods and is capable of identifying both faint and distinct clues for humans to debunk fake news.

Original languageEnglish
Title of host publicationECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings
EditorsKobi Gal, Kobi Gal, Ann Nowe, Grzegorz J. Nalepa, Roy Fairstein, Roxana Radulescu
PublisherIOS Press BV
Pages549-556
Number of pages8
ISBN (Electronic)9781643684369
DOIs
StatePublished - 28 Sep 2023
Event26th European Conference on Artificial Intelligence, ECAI 2023 - Krakow, Poland
Duration: 30 Sep 20234 Oct 2023

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume372
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference26th European Conference on Artificial Intelligence, ECAI 2023
Country/TerritoryPoland
CityKrakow
Period30/09/234/10/23

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