Trust-based Recommender System for Fake News Mitigation

The ubiquity of fake news has been a serious problem on the Internet. Recommender systems, in particular, contribute to this issue by creating echo chambers of misinformation. In light of these observations, we address the issue of fake news mitigation through the lens of recommender systems. This paper introduces a novel adaptation of the collaborative filtering algorithm that models untrustworthy online users in order to remove them from the candidate user's neighborhood. The proposed approach, FAke News Aware Recommender system (FANAR), is an alteration of the collaborative filtering strategy that considerably prevents the propagation of fake news by avoiding untrustworthy neighbors. Furthermore, we create FNEWR, a dataset for the Fake News Recommendation system, to fulfill our goal. Our experiments reveal that FANAR surpasses the current leading news recommendation techniques in its ability to suggest personalized news and mitigate the spread of false information.