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

Wang, S; Xu, X; Zhang, X; Wang, Y; Song, W

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

Wang, S

Xu, X Zhang, X Wang, Y Song, W

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Conference Proceeding
Citation:: WWW 2022 - Proceedings of the ACM Web Conference 2022, 2022, pp. 3673-3684
Issue Date:: 2022-04-25

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Field	Value	Language
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Xu, X
dc.contributor.author	Zhang, X
dc.contributor.author	Wang, Y
dc.contributor.author	Song, W
dc.date	2022-04-25
dc.date.accessioned	2022-11-12T10:31:17Z
dc.date.available	2022-11-12T10:31:17Z
dc.date.issued	2022-04-25
dc.identifier.citation	WWW 2022 - Proceedings of the ACM Web Conference 2022, 2022, pp. 3673-3684
dc.identifier.isbn	9781450390965
dc.identifier.uri	http://hdl.handle.net/10453/163398
dc.description.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.
dc.language	en
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	WWW 2022 - Proceedings of the ACM Web Conference 2022
dc.relation.ispartof	31st ACM Web Conference (WWW)
dc.relation.isbasedon	10.1145/3485447.3512263
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Veracity-aware and Event-driven Personalized News Recommendation for Fake News Mitigation
dc.type	Conference Proceeding
utslib.location.activity	ELECTR NETWORK
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-11-12T10:31:16Z
pubs.finish-date	2022-04-29
pubs.publication-status	Published
pubs.start-date	2022-04-25

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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http://hdl.handle.net/10453/163398