  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.
