基于图卷积网络的谣言鉴别研究  

Rumor Identification Research Based on Graph Convolutional Network

作　　者：米源 唐恒亮[1,2] MI Yuan;TANG Hengliang(School of Information,Beijing Wuzi University,Beijing 101149,China;Beijing Key Laboratory of Multimedia and Intelligent Software Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京物资学院信息学院,北京101149 [2]北京工业大学多媒体与智能软件技术北京市重点实验室,北京100124

出　　处：《计算机工程与应用》2021年第13期161-167,共7页Computer Engineering and Applications

基　　金：北京市高创计划青年拔尖人才项目(2017000026833ZK25);北京市通州区运河计划领军人才项目(YHLB2017038);北京物资学院基层学术团队建设项目(2019XJJCTD04)。

摘　　要：随着大数据时代的演进,互联网中的谣言成井喷状涌现。目前网络谣言鉴别方法中,基于监督学习的模型在训练过程中需要大量标注数据,同时网络谣言的人工标注用时较长,故提出采用半监督学习的图卷积神经网络,可有效利用无标注数据。通过在有标注节点上训练模型,更新所有节点共享的权重矩阵,将有标注节点信息传播给无标注节点,同时解决监督学习模型泛化能力不强和无监督学习模型不稳定的问题。与基于SVM算法、逻辑回归算法和BiLSTM模型的三种网络谣言鉴别方法相比,该方法在召回率、F1值两个评价指标上分别达到86.1%、85.3%,进一步提升了网络谣言鉴别的准确性和稳定性。该方法可有效减少人工标注代价,鉴别社交媒体和网络新闻中的谣言,为网络谣言的治理提供新思路。With the evolution of the era of big data,rumors in the Internet have sprung up.In current rumor identification methods,supervised learning models require a large amount of labeled data during training,and the manual labeling of network rumors takes a long time.Therefore,this paper proposes a semi-supervised graph convolutional neural network,which can effectively use unlabeled data.The model trains on labeled nodes,updates the weight matrix shared by all nodes,and propagates the information of labeled nodes to unlabeled nodes.At the same time,it solves the problem of weak generalization of the supervised learning model and the instability of the unsupervised learning model.Compared with three network rumor identification methods based on SVM algorithm,logistic regression algorithm and BiLSTM model,respectively,the method in this paper reaches 86.1%and 85.3%in the two evaluation indicators of recall rate and F1 value,which further improves the accuracy and stability of network rumor identification.The method can effectively reduce the cost of manual tagging,identify rumors in social media and online news,and provide new ideas for the man-agement of network rumors.

关 键 词：谣言鉴别 半监督学习 图卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]