DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

By Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, Gerhard Weikum. 2018

367 citations recorded by the source corpus.

Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, background): “Here, models either highlight salient subsequences from the evidence (Popat et al., 2018; Wu et al., 2020), generate summaries (Kotonya and Toni, 2020; Atanasova et al.”
Survey of Hallucination in Natural Language Generation (2022, background): “, web traffic, PageRank or URL structure) have been proposed to deal with this trustworthiness issue [7, 144, 145].”
Explainable Automated Fact-Checking for Public Health Claims (2020, background): “, 2019), saliency maps (Shu et al., 2019a; Popat et al., 2018), and natural language generation (Atanasova et al.”
Generating Fact Checking Briefs (2020, critical): “Popat et al. (2018) and Augenstein et al. (2019) used search engines, but without ensuring that they provide evidence supporting/refuting the claim instead of being re-lated to it or that they were not fact checking reports.”
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (2021, background): “Most of the fact veriﬁcation literature focuses on resolving claims to be true or false (Popat et al., 2018; Thorne and Vlachos, 2018; Wang, 2017).”
Hate speech detection: Challenges and solutions (2019, neutral): “verification—another difficult task [15].”
The Limitations of Stylometry for Detecting Machine-Generated Fake News (2019, background): “This approach have recently gained increasing attention thanks to several synthetic (Thorne et al. 2018) and real-world datasets (Hanselowski et al. 2018; Wang 2017; Popat et al. 2018; Augen-stein et al. 2019).”
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020, background): “…Wu et al., 2020), emotions (Ajao et al., 2019), stances (Ma et al., 2018a; Kochkina et al., 2018; Wu et al., 2019), write styles (Potthast et al., 2018; Gr¨ondahl and Asokan, 2019), and source credibility (Popat et al., 2018; Baly et al., 2018a) from claims and relevant articles (or comments).”
When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection (2025, unknown): “Popat et al. (2018) introduced an end-to-end neural network model for debunking fake news and false claims.”