Few-shot Hate Speech Detection Based on the MindSpore Framework

• URL: http://arxiv.org/abs/2504.15987v1
• Date: Tue, 22 Apr 2025 15:42:33 GMT
• Title: Few-shot Hate Speech Detection Based on the MindSpore Framework
• Authors: Zhenkai Qin, Dongze Wu, Yuxin Liu, Guifang Yang,
• Abstract summary: We propose MS-Hate, a prompt-enhanced neural framework for few-shot hate speech detection implemented on the MindSpore deep learning platform.<n> Experimental results on two benchmark datasets-HateXplain and HSOL-demonstrate that our approach outperforms competitive baselines in precision, recall, and F1-score.<n>These findings highlight the potential of combining prompt-based learning with adversarial augmentation for robust and adaptable hate speech detection in few-shot scenarios.
• Score: 2.6396343924017915
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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