dEFEND: A system for explainable fake news detection

Abstract

Despite recent advancements in computationally detecting fake news, we argue that a critical missing piece be the explainability of such detection-i.e., why a particular piece of news is detected as fake-and propose to exploit rich information in users' comments on social media to infer the authenticity of news. In this demo paper, we present our system for an explainable fake news detection called dEFEND, which can detect the authenticity of a piece of news while identifying user comments that can explain why the news is fake or real. Our solution develops 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. The system is publicly accessible.