Rumor Detection on Hierarchical Attention Network with User and Sentiment Information

Social media has developed rapidly due to its openness and freedom, and people can post information on Internet anytime and anywhere. However, social media has also become the main way for rumors to spread largely and quickly. Hence, it has become a huge challenge to automatically detect rumors among such a huge amount of information. Currently, there are many neural network methods, which mainly considered text features but did not pay enough attention to user and sentiment information that are also useful clues for rumor detection. Therefore, this paper proposes a hierarchical attention network with user and sentiment information (HiAN-US) for rumor detection, which first uses the transformer encoder to learn the semantic information at both word-level and tweet-level, then integrates user and sentiment information via attention mechanism. Experiments on the Twitter15, Twitter16 and PHEME datasets show that our model is more effective than several state-of-the-art baselines.