论文标题
GCAN：图形感知的共同注意网络，用于在社交媒体上解释的假新闻检测
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
论文作者
论文摘要
本文在社交媒体上更现实的情况下解决了假新闻检测问题。鉴于源短文本推文和没有文本注释的相应转发用户的相应顺序，我们旨在预测源推文是否是假的，并通过突出可疑转推人的证据以及他们所关注的单词来生成解释。我们开发了一种基于神经网络的新型模型，图形感知的共同注意网络（GCAN），以实现目标。在实际推文数据集上进行的广泛实验展示了GCAN可以平均胜过最先进的方法16％。此外，案例研究还表明，GCAN可以产生合理的解释。
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.
