The proliferation of hate speech on social media poses a significant threat to online communities, requiring effective detection systems. While deep learning models have shown promise, their performance often deteriorates in few-shot or low-resource settings due to reliance on large annotated corpora. To address this, we propose MS-FSLHate, a prompt-enhanced neural framework for few-shot hate speech detection implemented on the MindSpore deep learning platform. The model integrates learnable prompt embeddings, a CNN-BiLSTM backbone with attention pooling, and synonym-based adversarial data augmentation to improve generalization. Experimental results on two benchmark datasets-HateXplain and HSOL-demonstrate that our approach outperforms competitive baselines in precision, recall, and F1-score. Additionally, the framework shows high efficiency and scalability, suggesting its suitability for deployment in resource-constrained environments. These findings highlight the potential of combining prompt-based learning with adversarial augmentation for robust and adaptable hate speech detection in few-shot scenarios.

本研究解决了社交媒体上仇恨言论检测在少量样本或低资源环境中表现不佳的问题。提出了一种名为MS-FSLHate的框架，该框架结合了可学习提示嵌入、CNN-BiLSTM骨干网络和同义词对抗数据增强，显著提高了检测的准确性和适应性。实验结果表明，该方法在精确率、召回率和F1值上超越了竞争基准，并适合在资源有限的环境中部署。

基于MindSpore框架的少量样本仇恨言论检测