A Rumor Detection Approach Based on Multi-Feature Propagation Tree
At present,rumor detection methods on social platforms mainly focus on obtaining information from the propagation path,most of these methods only use text information as the initial propagation feature,which is difficult to cap-ture the rich propagation structure representation.In this paper,according to the propagation path of rumors,text and user credibility features are extracted,and a multi-feature rumor detection model based on propagation tree is constructed.This model aggregates text propagation features through a graph convolutional network,and uses a multi-head attention module to mine the intra-layer dependencies of the text propagation tree.At the same time,a credibility sequence is constructed for each user in the user propagation tree,and the M-Attention module is used to capture effective user credibility features.The experimental results show that the experimental accuracy of Twitter15,Twitter16 and Weibo datasets reaches 89.3%,91.7%and 96.4%,which are 4.8%,4.2%and 3%higher than the current optimal propagation tree model Bi-GCN(Binary Graph Convolutional Network)accuracy respectively.
国家科技期刊平台
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