计算机科学
注意力网络
社会化媒体
图形
假新闻
情报检索
人工神经网络
社交网络(社会语言学)
社会网络分析
人工智能
机器学习
理论计算机科学
万维网
互联网隐私
标识
DOI:10.18653/v1/2020.acl-main.48
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI