传播路径树核学习的微博谣言检测方法  

Microblog Rumor Detection Method Based on Propagation Path Tree Kernel Learning

作　　者：徐建民 孙朋 吴树芳[2] XU Jian-min;SUN Peng;WU Shu-fang(School of Cyberspace Security and Computer,Hebei University,Baoding,Hebei 071002,China;School of Management,Hebei University,Baoding,Hebei 071002,China)

机构地区：[1]河北大学网络空间安全与计算机学院,河北保定071002 [2]河北大学管理学院,河北保定071002

出　　处：《计算机科学》2022年第6期342-349,共8页Computer Science

基　　金：国家社会科学基金(17BTQ068)。

摘　　要：微博等在线社交平台的迅猛发展,促进了各种谣言信息的广泛传播,进而给社会秩序带来了潜在的威胁。微博谣言检测能够有效遏制谣言的传播,对净化网络环境、维护社会安定具有重要意义。针对传统谣言检测模型仅考虑用户、内容、传播统计等特征,忽略了谣言传播过程中用户的影响力、情感反馈等特征随转发和评论关系变化而变化的结构问题,提出了一种基于微博信息传播树的路径树核谣言检测模型。所提模型将用户的影响力、情感反馈和内容等特征嵌入传播树的节点中,通过计算传播树中从根节点到叶子节点的路径相似度,得到微博信息传播树结构之间的相似度,进而使用基于传播路径树核的支持向量机实现对微博谣言的检测。实验结果显示,所提模型的准确率达到93.5%,其效果优于未考虑传播路径结构特征的谣言检测模型。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.

关 键 词：传播路径 传播树 微博谣言检测 核方法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]