一种基于BLRQ-BV模型的社交媒体谣言检测方法及系统
By employing a multimodal feature fusion method based on the BLRQ-BV model, the problems of semantic integrity loss and discriminative information loss in social media rumor detection are solved, achieving high-precision and robust rumor detection. This method is applicable to practical application scenarios such as network content security management, public opinion monitoring, and digital forensics.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NINGXIA UNIVERSITY
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for detecting rumors on social media suffer from problems such as fragmented event semantics, loss of feature spatial relationships during multimodal fusion, and loss of discriminative information in quantum hybrid networks, resulting in low detection accuracy and poor robustness.
The BLRQ-BV model is adopted, and the sequential features of the text modality are extracted by BiLSTM, the high discriminative quantum features are extracted by LIL-QHN, and the visual features of the image modality are extracted by ResNet-18. The lossless fusion of the three modal features is achieved by the spherical space vector synthesis method (VC-BS), and finally the high-precision end-to-end rumor detection is achieved.
It achieves an average accuracy of 98.34% on the Twitter, Weibo, and PHEME datasets, significantly outperforming existing technologies, and demonstrates high classification accuracy, strong robustness, and good cross-language generalization ability.
Smart Images
Figure CN122087736B_ABST