Paper Info
Title
Veracity-aware and Event-driven Personalized News Recommendation for Fake News Mitigation
Abstract
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.
Year
DOI
Venue
2022
10.1145/3485447.3512263
International World Wide Web Conference
Keywords
DocType
Citations 
Fake news mitigation, News recommendation, Recommender systems, Fake news detection
Conference
0
PageRank 
References 
Authors
0.34
25
5
Name
Order
Citations
PageRank
Shoujin Wang16513.10
Xiaofei Xu240870.26
Xiuzhen Zhang3348.69
Yan Wang4105478.15
Wenzhuo Song500.34