Detecting Weibo Rumors Based on Hierarchical Semantic Feature Learning Model

[Objective] This paper tries to improve the accuracy and timeliness of Weibo rumor detection. [Methods] We proposed a rumor detection method based on the hierarchical semantic feature learning model (BCGA). Firstly, we extracted the semantic features of a single text in an event based on the BERT model. Secondly, we dynamically grouped the event propagation data based on the time domain. Next, we used the convolutional neural network to learn the semantic correlation features of the text sets in each time domain. Fourth, we input the semantic correlation features in each time domain into the deep bidirectional gated recurrent neural network to learn the deep semantic temporal features of the event propagation process. Finally, we integrated the attention mechanism to make the model focus on the rumor feature in semantic temporal features. [Results] Experiments on the Weibo public data sets show that the detection accuracy of the model reached 95.39%, while the detection delay was within 12 hours. [Limitations] The model requires a certain amount of forwarding and commenting information and the detection effect is not prominent when the event is not popular enough. [Conclusions] The hierarchical semantic feature learning model achieves a learning process from local to global semantics, improving the performance of Weibo rumor detection. © 2023 Editorial Department of Science and Technology of Food Science.