Rumor detection in social media based on a hierarchical attention network

Abstract

For rumor detection in social media, the majority of feature-representation-based studies capture textual features or global users features according to a partitioned sequence of microblogs. However, these studies ignore the time-series information across the microblogs in one time interval. Moreover, the latent textual information and the local users information, which have been proven effective according to the traditional machine learning methods, are overlooked in capturing the time intervals features, resulting in low performance. Therefore, we propose a rumor detection method in social media based on a hierarchical attention network. First, microblogs are partitioned into several time intervals. Then, the variation of information across the microblogs changing over time is learned using a bidirectional gated recurrent unit neural network with an attention mechanism. After that, the variation of features across the microblogs is combined with hand-crafted features to incorporate latent textual information and local users information into the time intervals features. Finally, we capture features variation across the time intervals by using the bidirectional gated recurrent unit neural network, with the attention mechanism, and classify microblog events. Experimental results over two public datasets, Sina Weibo and Twitter, show that the proposed method outperforms (in terms of the accuracy) state-of-the-art methods by 1.5% and 1.4% over the two datasets, respectively, and it is effective for rumor detection.