Rumor detection based on a Source-Replies conversation Tree Convolutional Neural Net

Na Bai, Fanrong Meng, Xiaobin Rui, Zhixiao Wang

Abstract: Rumor detection is a hot research topic in social networks. It is challenging to simultaneously extract content features and structural features from rumor conversations to detect rumors, and many existing methods only focus on one of them. In this paper, we propose a Source-Replies conversation Tree Convolutional Neural Net (TCN) to extract these two features simultaneously for the rumor detection task. Specifically, we first build Source-Replies conversation Trees (SR-Trees) based on rumor conversations, and then we construct an SR-Tree-based Auto-Encoder (TAE) on SR-Trees. A TAE designs Spatial Tree Convolution and Tree-pooling to build an effective feature extractor to extract content features and structural features from SR-Trees. Based on the pre-trained feature extractor in the TAE, an end-to-end TCN is proposed to detect rumors. In experiments, we verify that compared with other commonly used Rumor analysis models, the proposed TCN is effective on the rumor detection task.