GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

TL;DR

This paper introduces GCAN, a neural network model that detects fake news on social media by analyzing retweeters and their concerned words, providing both accurate predictions and explainable evidence highlighting suspicious users and words.

Contribution

The paper proposes a novel Graph-aware Co-Attention Network (GCAN) that improves fake news detection accuracy and offers explainability by identifying key retweeters and words.

Findings

GCAN outperforms state-of-the-art methods by 16% in accuracy.

GCAN provides reasonable explanations for its predictions.

The model effectively analyzes retweeters and their concerned words.

Abstract

This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.