Multi-feature Fusion Rumor Detection Method Based on Graph Convolutional Network

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Multi-feature Fusion Rumor Detection Method Based on Graph Convolutional Network

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[1]GUAN Changshan,BING Wanlong,LIU Yahui,et al.Multi-feature Fusion Rumor Detection Method Based on Graph Convolutional Network[J].Journal of Zhengzhou University (Engineering Science),2024,45(04):70-78.[doi:10.13705/ j.issn.1671-6833.2024.01.011]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 04 Page number: 70-78 Column: Public date: 2024-06-16

Title:: Multi-feature Fusion Rumor Detection Method Based on Graph Convolutional Network

Author(s):: GUAN Changshan; BING Wanlong; LIU Yahui; GU Pengfei; MA Hongliang; School of Information Science and Technology, Shihezi University, Shihezi 832003, China

Keywords:: rumor detection; graph convolutional network; propagation graph; propagation user; feature fusion

CLC:: TP18

DOI:: 10.13705/ j.issn.1671-6833.2024.01.011

Abstract:: At present, most rumor detection work mainly based on the original text content, communication struc ture and communication text content of Twitter or Weibo. However, these methods ignored the effective integration of original text features with other features, as well as the role of propagating users in the process of rumor propaga tion. Aiming at the shortcomings of the existing work, a multi-feature fusion model GCNs-BERT based on graph convolutional network was proposed, which combined the features of the original text, the propagating user and the propagating structure. Firstly, a propagation graph was constructed based on the propagation structure and the prop agation users, and the combination of multiple user attributes was used as the propagation node feature. Then, mul tiple graph convolutional networks were used to learn the representation of the propagation graph with different user attribute combinations, and BERT model was used to learn the feature representation of the original text content. Finally, the fusion with the features learned by the graph convolutional network was used to detect rumors. A large number of experiments using publicly available Weibo data sets showed that the GCNs-BERT model was significant ly better than the baseline method. In addition, the generalization ability experiment of GCNs-BERT model was conducted on the novel coronavirus epidemic data set. The training sample size of this data set was only 1/5 of that of the public Weibo data set, and the accuracy rate was still 92.5%, which proved that the model had good gener alization ability.

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Last Update: 2024-06-14