As rumors continue to diffuse and spread on the network,their influence will become more and more serious.In the earliest stage that rumors have not yet spread,it is of great significance to use the user information and text informa-tion of the source account to identify and suppress them.The current detection methods are limited to natural language processing technology and focus on extracting information from the text to identify rumors.The lack of in-depth mining and an effective combination of user information leads to low detection performance of the model.In this paper,we pro-pose a new approach for early rumor detection,RT-NIG,which can identify rumors by reconstructing two-tuples informa-tion in the cross-distributed neighborhood information graphs.Firstly,in response to the situation where there is a lack of dissemination information in the initial stage of rumor propagation and graph-structured data cannot be formed,a virtual neighborhood graph is constructed using the potential correlation of objects to solve the problems of data uncertainty and incompleteness.Then the potential object relationships in the neighborhood graph are captured by graph neural network.The user information and semantic information are transferred in two neighborhood information graphs and reconstructed,paying attention to the potential credibility relationship between users and the emotional polarity relationship between texts.Finally,using weighted integration,the"User-Tweet"two-tuple information is reconstructed,effectively combining the two kinds of information,and is used for the downstream rumor classification task.Experiments are conducted on two real datasets,Chinese Weibo and English PHEME.The proposed method outperformed various advanced early detection methods in accuracy,accuracy,recall,F1 value,and other indicators.The accuracy was improved by 5%and 8%compared to the optimal comparison method on the two datasets.Ablation research and super parameter analysis further prove the important role of user information in early detection and the effectiveness of two-tuple information reconstruction.RT-NIG also provides new solutions for other early detection problems,such as fake news,online violence,misleading information,etc.,in scenarios where no disseminated structural information is available.
关键词
谣言早期检测/RT-NIG/邻域信息图/重构二元组/图神经网络
Key words
early rumor detection/RT-NIG/neighborhood information graphs/reconstruct two-tuples/GNN
维普
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