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

Keywords

MSC

68T50

Cite this article

@article{cai-7155,
  author  = {Yu Zhu and Gensheng Wang and Xuejian Huang and Wenwen Jin and Sheng Li},
  title   = {Enhancing Real-Time Rumor Detection on Weibo Through User and Content Feature Integration},
  journal = {Computing and Informatics},
  volume  = {44},
  number  = {5},
  pages   = {1229--1253},
  year    = {2025},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2025_5_1229}
}
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