HGAT: Hierarchical Graph Attention Network for Fake News Detection

The explosive growth of fake news has eroded the credibility of medias and governments. Fake news detection has become an urgent task. News articles along with other related components like news creators and news subjects can be modeled as a heterogeneous information network (HIN for short). In this paper, we focus on studying the HIN- based fake news detection problem. We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network (HGAT) which employs a novel hierarchical attention mechanism to detect fake news by classifying news article nodes in the HIN. This method can effectively learn information from different types of related nodes through node-level and schema-level attention. Experiments with real-world fake news data show that our model can outperform text-based models and other network-based models. Besides, the experiments also demonstrate the expandability and potential of HGAT for heterogeneous graphs representation learning in the future.