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

TL;DR

DeClarE introduces an end-to-end neural network model that assesses the credibility of textual claims by integrating external evidence, source trustworthiness, and explanation generation, advancing automated fact-checking without manual feature engineering.

Contribution

This work presents a novel neural network approach that automatically incorporates external evidence and source trustworthiness for fact-checking, eliminating the need for manual feature design.

Findings

Outperforms existing methods on four datasets.

Provides transparent, user-friendly explanations.

Effective evidence aggregation improves credibility assessment.

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.