用零样本方法识别网络不当言论，无需标注数据即可有效检测极端话语。
Analysis of Socially Unacceptable Discourse with Zero-shot Learning
- 基于文本蕴含的零样本分类，利用预训练模型和提示技术
- 在未见过的数据上展现良好泛化能力，适合极端话语分析
- 无需人工标注，适合快速构建标注数据集
社交不端言论（SUD）分析对维护网络正面环境至关重要。本文通过利用预训练的Transformer模型与提示技术，研究基于文本蕴含的零样本文本分类（无监督方法）在SUD检测与表征中的有效性。实验结果表明，这些模型对未见数据具有良好的泛化能力，凸显该方法在生成极端叙事分析标注数据集方面的巨大潜力。研究成果有助于开发更稳健的SUD分析工具，促进负责任的在线沟通。
原文摘要 · Abstract (English)
Socially Unacceptable Discourse (SUD) analysis is crucial for maintaining online positive environments. We investigate the effectiveness of Entailment-based zero-shot text classification (unsupervised method) for SUD detection and characterization by leveraging pre-trained transformer models and prompting techniques. The results demonstrate good generalization capabilities of these models to unseen data and highlight the promising nature of this approach for generating labeled datasets for the analysis and characterization of extremist narratives. The findings of this research contribute to the development of robust tools for studying SUD and promoting responsible communication online.
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