With the development of network and communication technology,rumors spread rapidly like virus with in Weibo and other platforms,causing serious risks to national security and social stability.In order to improve the ac-curacy of automatic rumor detection,we present a global-local attention network for rumor detection model.Firstly,user features are introduced as higher-order features in addition to the text information and propagation structure features of Weibo.Secondly,the graph attention network is improved to obtain more robust node aggregation fea-tures,which provides more accurate evidence for judging a rumor.The experimental results on the Weibo rumor dataset show that the detection model proposed in this paper has higher accuracy than the existing algorithms.
关键词
谣言检测/图注意力机制/用户属性信息/传播结构信息
Key words
rumor detection/graph attention network/user features/structure propagation information
维普
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