用法律知识提升可起诉仇恨言论检测效果
Hate Speech According to the Law: An Analysis for Effective Detection
- 结合法律专家标注与大模型，利用法律知识增强检测能力
- 伪标签技术缓解可起诉仇恨言论数据稀缺问题
- 强调各国法律差异对检测策略的关键影响
仇恨言论不仅存在于网络空间，更带来现实后果，促使多数国家将仇恨言论定为可诉罪。然而各国法律定义差异导致平台在处理举报时面临巨大混乱。现有仇恨言论定义难以构建稳健框架，因此本文转向研究可起诉仇恨言论的法律规范。通过咨询法律专家并基于仇恨言论数据集进行标注，实验采用预训练模型及两大语言模型（Qwen2-7B-Instruct 和 Meta-Llama-3-70B）开展分析。由于可起诉仇恨言论数据获取耗时，采用伪标签技术优化模型性能。研究发现，法律知识形式的标注有助于提升可起诉仇恨言论分类效果，但更需关注各国法律差异带来的影响。本研究强调应加强可起诉仇恨言论的研究，并为依法治理提供有效策略。
原文摘要 · Abstract (English)
The issue of hate speech extends beyond the confines of the online realm. It is a problem with real-life repercussions, prompting most nations to formulate legal frameworks that classify hate speech as a punishable offence. These legal frameworks differ from one country to another, contributing to the big chaos that online platforms have to face when addressing reported instances of hate speech. With the definitions of hate speech falling short in introducing a robust framework, we turn our gaze onto hate speech laws. We consult the opinion of legal experts on a hate speech dataset and we experiment by employing various approaches such as pretrained models both on hate speech and legal data, as well as exploiting two large language models (Qwen2-7B-Instruct and Meta-Llama-3-70B). Due to the time-consuming nature of data acquisition for prosecutable hate speech, we use pseudo-labeling to improve our pretrained models. This study highlights the importance of amplifying research on prosecutable hate speech and provides insights into effective strategies for combating hate speech within the parameters of legal frameworks. Our findings show that legal knowledge in the form of annotations can be useful when classifying prosecutable hate speech, yet more focus should be paid on the differences between the laws.
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