research-article

VICTOR: An Implicit Approach to Mitigate Misinformation via Continuous Verification Reading

Authors:

Shih-Chieh Dai,

Lun-Wei KuAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3511 - 3519

https://doi.org/10.1145/3485447.3512246

Published: 25 April 2022 Publication History

Abstract

We design and evaluate VICTOR, an easy-to-apply module on top of a recommender system to mitigate misinformation. VICTOR takes an elegant, implicit approach to deliver fake-news verifications, such that readers of fake news can continuously access more verified news articles about fake-news events without explicit correction. We frame fake-news intervention within VICTOR as a graph-based question-answering (QA) task, with Q as a fake-news article and A as the corresponding verified articles. Specifically, VICTOR adopts reinforcement learning: it first considers fake-news readers’ preferences supported by underlying news recommender systems and then directs their reading sequence towards the verified news articles. To verify the performance of VICTOR, we collect and organize VERI, a new dataset consisting of real-news articles, user browsing logs, and fake-real news pairs for a large number of misinformation events. We evaluate zero-shot and few-shot VICTOR on VERI to simulate the never-exposed-ever and seen-before conditions of users while reading a piece of fake news. Results demonstrate that compared to baselines, VICTOR proactively delivers 6% more verified articles with a diversity increase of 7.5% to over 68% of at-risk users who have been exposed to fake news. Moreover, we conduct a field user study in which 165 participants evaluated fake news articles. Participants in the VICTOR condition show better exposure rates, proposal rates, and click rates on verified news articles than those in the other two conditions. Altogether, our work demonstrates the potentials of VICTOR, i.e., combat fake news by delivering verified information implicitly.

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Cited By

Pathak RSpezzano FPera M(2023)Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social NetworksACM Transactions on the Web10.1145/361608817:4(1-26)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3616088
Pendyala V(2022)Misinformation Containment Using NLP and Machine LearningDeep Learning Research Applications for Natural Language Processing10.4018/978-1-6684-6001-6.ch003(41-56)Online publication date: 9-Dec-2022
https://doi.org/10.4018/978-1-6684-6001-6.ch003

Index Terms

VICTOR: An Implicit Approach to Mitigate Misinformation via Continuous Verification Reading

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
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Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
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Ivan Herman
W3C / retired
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Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Pathak RSpezzano FPera M(2023)Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social NetworksACM Transactions on the Web10.1145/361608817:4(1-26)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3616088
Pendyala V(2022)Misinformation Containment Using NLP and Machine LearningDeep Learning Research Applications for Natural Language Processing10.4018/978-1-6684-6001-6.ch003(41-56)Online publication date: 9-Dec-2022
https://doi.org/10.4018/978-1-6684-6001-6.ch003

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