dEFEND: Explainable Fake News Detection
Kai Shu,Limeng Cui,Suhang Wang,Dongwon Lee,Huan Liu +4 more
- pp 395-405
TLDR
A sentence-comment co-attention sub-network is developed to exploit both news contents and user comments to jointly capture explainable top-k check-worthy sentences and userComments for fake news detection.Abstract:
In recent years, to mitigate the problem of fake news, computational detection of fake news has been studied, producing some promising early results. While important, however, we argue that a critical missing piece of the study be the explainability of such detection, i.e., why a particular piece of news is detected as fake. In this paper, therefore, we study the explainable detection of fake news. We develop a sentence-comment co-attention sub-network to exploit both news contents and user comments to jointly capture explainable top-k check-worthy sentences and user comments for fake news detection. We conduct extensive experiments on real-world datasets and demonstrate that the proposed method not only significantly outperforms 7 state-of-the-art fake news detection methods by at least 5.33% in F1-score, but also (concurrently) identifies top-k user comments that explain why a news piece is fake, better than baselines by 28.2% in NDCG and 30.7% in Precision.read more
Citations
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Journal ArticleDOI
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media
TL;DR: A fake news data repository FakeNewsNet is presented, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information, and is discussed for potential applications on fake news study on social media.
Journal ArticleDOI
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Xinyi Zhou,Reza Zafarani +1 more
TL;DR: In this article, the authors present a survey of methods that can detect fake news from four perspectives: the false knowledge it carries, its writing style, its propagation patterns, and the credibility of its source.
Proceedings ArticleDOI
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
Yi-Ju Lu,Cheng-Te Li +1 more
TL;DR: This paper solves the fake news detection problem under a more realistic scenario on social media by developing a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), which can significantly outperform state-of-the-art methods by 16% in accuracy on average.
Journal ArticleDOI
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Xinyi Zhou,Reza Zafarani +1 more
TL;DR: In this paper, a survey of methods that can detect fake news from four perspectives: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its source.
Journal ArticleDOI
FakeBERT: Fake news detection in social media with a BERT-based deep learning approach.
TL;DR: FakeBERT as discussed by the authors combines different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT, which is useful to handle ambiguity.
References
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