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

Abstract

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.

arXiv commentEMNLP 2018

References in corpus (3)

• Adam: A Method for Stochastic Optimization
• Towards Universal Paraphrastic Sentence Embeddings
• People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities

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• Graph Neural Networks with Continual Learning for Fake News Detection from Social Media
• The Future of Misinformation Detection: New Perspectives and Trends
• Misinformation Has High Perplexity
• Technology-Enabled Disinformation: Summary, Lessons, and Recommendations
• Open Issues in Combating Fake News: Interpretability as an Opportunity
• Weak Supervision for Fake News Detection via Reinforcement Learning
• Learning from Fact-checkers: Analysis and Generation of Fact-checking Language
• The Mass, Fake News, and Cognition Security
• Towards Explainable Fact Checking
• Claim Verification using a Multi-GAN based Model