Rumor Detection Model Based on Dynamic Propagation and Community Structure

To address the insufficient utilization of time information, and to verify that the community structure features of rumor propagation can improve the performance of rumor detection models. Dy_PCRD (rumor detection model based on dynamic propagation and community structure) model is proposed which integrates dynamic propagation and community structure. On the one hand, GCN can extract structural features of rumor propagation, and on the other hand, topic communities are divided based on rumor content and propagation structure, and then a new attention calculation method is used to extract community structural features. They are inputted into temporal attention units to model their dynamic changes and classify the rumors. The experimental results on three public datasets show that under the same condition, its accuracy and other evaluation indicators have been improved compared to the baseline model, verifying the effectiveness of community structure features, dynamic features, and related attention calculation methods in improving the performance of rumor detection models. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.