Entity-Aware Dual Co-Attention Network for Fake News Detection

TL;DR

This paper introduces a Dual Co-Attention Network that leverages news content, social media replies, and external knowledge to improve fake news detection, demonstrating superior performance on benchmark datasets.

Contribution

The paper presents a novel Dual Co-Attention Network that effectively integrates multiple information sources for enhanced fake news detection.

Findings

Dual-CAN outperforms existing models on benchmark datasets

Attention weights provide insights into model decision-making

Empirical analysis reveals how models utilize different information sources

Abstract

Fake news and misinformation spread rapidly on the Internet. How to identify it and how to interpret the identification results have become important issues. In this paper, we propose a Dual Co-Attention Network (Dual-CAN) for fake news detection, which takes news content, social media replies, and external knowledge into consideration. Our experimental results support that the proposed Dual-CAN outperforms current representative models in two benchmark datasets. We further make in-depth discussions by comparing how models work in both datasets with empirical analysis of attention weights.