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arXiv · 2504.17574

RAGAT-Mind: A Multi-Granular Modeling Approach for Rumor Detection Based on MindSpore

Zhenkai Qin·Guifang Yang·Dongze Wu
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Abstract

As false information continues to proliferate across social media platforms, effective rumor detection has emerged as a pressing challenge in natural language processing. This paper proposes RAGAT-Mind, a multi-granular modeling approach for Chinese rumor detection, built upon the MindSpore deep learning framework. The model integrates TextCNN for local semantic extraction, bidirectional GRU for sequential context learning, Multi-Head Self-Attention for global dependency focusing, and Bidirectional Graph Convolutional Networks (BiGCN) for structural representation of word co-occurrence graphs. Experiments on the Weibo1-Rumor dataset demonstrate that RAGAT-Mind achieves superior classification performance, attaining 99.2% accuracy and a macro-F1 score of 0.9919. The results validate the effectiveness of combining hierarchical linguistic features with graph-based semantic structures. Furthermore, the model exhibits strong generalization and interpretability, highlighting its practical value for real-world rumor detection applications.

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Zhenkai Qin, Guifang Yang, Dongze Wu. 2025-04-24. RAGAT-Mind: A Multi-Granular Modeling Approach for Rumor Detection Based on MindSpore. https://arxiv.org/abs/2504.17574

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