  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.
