Rumor Detection from Social Media via Multi-Level Unreliable Propagation Structures
Current rumor detection research focuses on studying the directional characteristics of rumor propagation.To exploit the potential structural features of rumors,this paper proposes a multi-level dynamic propagation atten-tion networks(MDPAN)to detect rumors.This method learns the contributions of all connecting edges in the prop-agation graph through a node-level attention,dynamically focusing on useful propagation relationships for identifying rumors.The graph convolutional networks extracts different levels of propagation features,diffusion features,and global structural features of rumors,which are fused via attention-based pooling methods.Compared with the EB-GCN model on Twitter15,Twitter16 and Weibo16 datasets,the proposed method increases the overall accuracy by 2.1％,0.7％and 1.7％,respectively.
万方数据
维普
