Sina Microblog Rumor Detection Method Based on Weighted-graph Convolutional Network
WANG Xin-yan1，SONG Yu-rong1，SONG Bo2
1（College of Automation & College of Artificial Intelligence，Nanjing University of Posts and Telecommunications，Nanjing 210023，China）2（School of Modern Posts & Institute of Modern Posts，Nanjing University of Posts and Telecommunications，Nanjing 210003，China）
Abstract：In the era of information，widespread rumors greatly affect the daily life of people and even threaten social stability.So it has practical significance for the task of rumor detection.At present，rumor detection methods based on the deep learning model ignore the connection between events or the closeness of the connection，which affects the detection results in a certain degree.Considering the heterogeneity of the connection between events，describing the closeness of the connection as the weight of the edge，we propose a new Sina Microblog Rumor Detection method based on Weighted-Graph Convolutional Network（W-GCN）.In this method，we use W-GCN model to study the hidden representation of the nodes，and then classifies the nodes to finally complete the rumor detection task.Experiment results show that，compared with the existing rumor detection methods，the rumor detection method based on W-GCN model we proposed can improve the accuracy，precision，recall and F1-measure of rumor detection，that is to say，it can be more effective for rumor identification.
王昕岩,宋玉蓉,宋波. 一种加权图卷积神经网络的新浪微博谣言检测方法[J]. 小型微型计算机系统, 2021, 42(8): 1780-1786.
WANG Xin-yan,SONG Yu-rong,SONG Bo. Sina Microblog Rumor Detection Method Based on Weighted-graph Convolutional Network. Journal of Chinese Computer Systems, 2021, 42(8): 1780-1786.
2021, Vol. 42 
