Microblog Rumor Detection Method Based on Propagation Path Tree Kernel Learning

Abstract

The rapid development of online social platforms such as microblog promotes the widespread propagation of various rumors information,thereby posing potential threats to social order.Rumor detection on microblog can effectively curb the spread of rumors and is of great significance for purifying the network environment and maintaining social stability.In view of the fact that the traditional rumor detection model only considers the characteristics of users,contents and communication statistics,and ignores the structural problem that the characteristics of users′ influence and emotional feedback increase with the forwarding and comment relationship in the process of rumor communication,a path tree kernel rumor automatic detection model based on the microblog information propagation tree is proposed in this paper.It embeds users’ influence,emotional feedback,contents into the nodes ofpropagation tree.By calculating the path similarity from the root node to the leaf node in propagation tree,the similarity between the microblog information propagation tree structure is obtained.Furthermore,the model uses the support vector machine classifier based on the propagation path tree kernel todetect microblog rumors.Experimental results show that the accuracy of the proposed model reaches 93.5%,which is better than that of the rumor detection models without considering the structure of propagation path.

Keywords

• propagation path|propagation tree|microblog rumor detection|kernel method