dEFEND: Explainable Fake News Detection

By Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, Huan Liu. Knowledge Discovery and Data Mining, 2019

732 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/0b11343d4f247826e725a458465174aa2578e52a

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Citations in the corpus

A Survey on Automated Fact-Checking (2021, background): “Similarly, coattention (Shu et al., 2019; Lu and Li, 2020) and self-attention (Yang et al.”
Explainable Automated Fact-Checking: A Survey (2020, methodology): “Shu et al. (2019), Gad-Elrab et al. (2019), Atanasova et al. (2020a), Kotonya and Toni (2020), and Yang et al. (2019) employ human annotators to assess the quality of explanations generated by their models.”
Generating Fact Checking Explanations (2020, background): “Shu et al. (2019) study the interpretability of a system that fact checks full-length news pages by leveraging user comments from social platforms.”
A Survey of Fake News (2020, methodology): “…such as squared error [Ma et al. 2018] and cross-entropy [Zhang et al. 2018], using some optimization algorithm, such stochastic gradient descent (SGD) [Wang et al. 2018], Adam [Kingma and Ba 2014], and the alternating direction method of multipliers (ADMM) [Boyd et al. 2011; Shu et al. 2019c].”
User Perceptions and Trust of Explainable Machine Learning Fake News Detectors (2022, background): “…fake news detectors such as Propagation2Vec (Silva et al., 2021) and xFake (Yang et al., 2019) Conceptually, these fake news detectors define fake news as something that is verifiably false, and consider the news consumption ecosystem as part of the detection modeling (Shu et al., 2019).”
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020, background): “Intrinsic explainability (Shu et al., 2019; He et al., 2015; Zhang and Chen, 2018) is achieved by constructing self-explanatory models that incorporate explainability directly into their structures, which requires to build fully interpretable models for clearly expressing the explainable process.”
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020, methodology): “Typical approaches rely on the content of new articles to extract textual features, such as n-gram and bag of words, and apply supervised learning (e.g., random forest and support vector machine) for binary classiﬁcation (Shu et al., 2017).”
When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection (2025, unknown): “Shu et al. (2019) utilizes GRU-based model for veracity prediction with explanations.”