research-article

Veracity-aware and Event-driven Personalized News Recommendation for Fake News Mitigation

Authors:

Wenzhuo SongAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3673 - 3684

https://doi.org/10.1145/3485447.3512263

Published: 25 April 2022 Publication History

Abstract

Despite the tremendous efforts by social media platforms and fact-check services for fake news detection, fake news and misinformation still spread wildly on social media platforms (e.g., Twitter). Consequently, fake news mitigation strategies are urgently needed. Most of the existing work on fake news mitigation focuses on the overall mitigation on a whole social network while ignoring developing concrete mitigation strategies to deter individual users from sharing fake news. In this paper, we propose a novel veracity-aware and event-driven recommendation model to recommend personalised corrective true news to individual users for effectively debunking fake news. Our proposed model Rec4Mit (Recommendation for Mitigation) not only effectively captures a user’s current reading preference with a focus on which event, e.g., US election, from her/his recent reading history containing true and/or fake news, but also accurately predicts the veracity (true or fake) of candidate news. As a result, Rec4Mit can recommend the most suitable true news to best match the user’s preference as well as to mitigate fake news. In particular, for those users who have read fake news of a certain event, Rec4Mit is able to recommend the corresponding true news of the same event. Extensive experiments on real-world datasets show Rec4Mit significantly outperforms the state-of-the-art news recommendation methods in terms of the capability to recommend personalized true news for fake news mitigation.

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Index Terms

Veracity-aware and Event-driven Personalized News Recommendation for Fake News Mitigation

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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https://doi.org/10.3390/e26060516
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https://dl.acm.org/doi/10.1145/3637528.3671944
Pathak RSerra ESpezzano F(2024)Advancing Misinformation Awareness in Recommender Systems for Social Media Information IntegrityProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680259(5471-5474)Online publication date: 21-Oct-2024
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