Entity-Aware Dual Co-Attention Network for Fake News Detection
This addresses the problem of identifying and interpreting fake news for online platforms and users, representing an incremental improvement with a novel hybrid method.
The paper tackled fake news detection by proposing a Dual Co-Attention Network (Dual-CAN) that integrates news content, social media replies, and external knowledge, achieving superior performance over current models on two benchmark datasets.
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.