Rumor Detection with Hierarchical Social Attention Network

Microblogs have become one of the most popular communication tools for news sharing. However, due to its openness and lack of supervision, rumors could also be easily posted and propagated in microblogs, which could have serious consequences. Therefore, tools for automatic detection and verification of rumors in microblogs are very valuable. In this paper, we propose a novel hierarchical neural network combined with social information (HSA-BLSTM) for rumor detection. At first a hierarchical bi-directional long short-term memory model is built for representation learning. Then, the social contexts are incorporated into the network via attention mechanism. Test this model on two real-world datasets fromWeibo and Twitter demonstrate outstanding performance in both rumor detection and early detection scenarios.