Deep learning

Feature (linguistics)

Credibility

Internet privacy

Machine learning

Trustworthiness

Language model

Philosophy

Political science

Artificial intelligence

Psychology

Natural language processing

Misinformation

Fake news

Computer science

Linguistics

False accusation

Deep neural networks

Computer security

Artificial neural network

Social psychology

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.

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