融合多粒度特征与图结构，提升中文谣言检测准确率
RAGAT-Mind: A Multi-Granular Modeling Approach for Rumor Detection Based on MindSpore
- 分层提取局部、序列、全局与词共现结构特征
- 在微博谣言数据集上达99.2%准确率与0.9919宏F1
- 模型兼具强泛化性与可解释性，适合实际应用
随着虚假信息在社交媒体平台持续扩散，有效谣言检测已成为自然语言处理中的紧迫挑战。本文提出RAGAT-Mind，一种基于MindSpore框架的中文谣言检测多粒度建模方法。该模型结合TextCNN提取局部语义，双向GRU学习序列上下文，多头自注意力捕捉全局依赖，以及双向图卷积网络（BiGCN）表征词共现图结构。在Weibo1-Rumor数据集上的实验表明，RAGAT-Mind达到99.2%的分类准确率和0.9919的宏F1分数，验证了融合层次化语言特征与图式语义结构的有效性。此外，模型展现出优异的泛化能力与可解释性，凸显其在真实场景中谣言检测的应用价值。
原文摘要 · Abstract (English)
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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