Enhancing Real-Time Rumor Detection on Weibo Through User and Content Feature Integration

Abstract

Weibo has emerged as a vital platform for Chinese netizens to share information, but it has also given rise to numerous rumors. Real-time detection methods that do not rely on propagation features are the most effective way to curb the spread of these rumors. Currently, real-time detection methods that mine semantic features of rumor text based on deep learning lack sufficient generalization ability. Therefore, we propose a real-time rumor detection method integrating multiple user and content features. In addition to standard user basic features, our approach utilizes the user's historical posting data to extract two deep-level features: user rationality and professionalism. Regarding content features, in addition to standard statistical features, we use a graph attention network that considers edge weights to learn deep semantic features of the content. The user and content features are concatenated and fed into a multi-layer perceptron for classification. The experimental results on a real Weibo dataset show that the accuracy of the proposed method achieves 92.6%, which outperforms all the compared baseline methods.