法律视角下的仇恨言论：面向有效检测的分析
Hate Speech According to the Law: An Analysis for Effective Detection
AI总结：
针对现有仇恨言论定义不足、各国法律差异导致平台治理混乱的问题，本研究结合法律专家标注与多类模型实验，验证了法律知识对可起诉仇恨言论分类的作用，为合法打击仇恨言论提供策略参考。
摘要：
仇恨言论问题的影响范围超出了网络空间的边界。这一问题会带来现实层面的后果，促使多数国家制定法律框架，将仇恨言论归类为可处罚的违法行为。这些法律框架因国家而异，加剧了网络平台在处理被举报的仇恨言论事件时面临的巨大混乱。鉴于现有仇恨言论定义不足以构建起稳健的框架，我们将研究目光投向仇恨言论相关法律。我们就一个仇恨言论数据集咨询了法律专家的意见，并采用多种方法开展实验，包括在仇恨言论数据与法律数据上训练的预训练模型，同时还调用了两个大语言模型（Qwen2-7B-Instruct与Meta-Llama-3-70B）。由于可起诉仇恨言论的数据采集十分耗时，我们使用伪标注方法来优化预训练模型。本研究强调了强化可起诉仇恨言论相关研究的重要性，并为在法律框架范围内打击仇恨言论的有效策略提供了洞见。我们的研究结果表明，以标注形式呈现的法律知识对可起诉仇恨言论的分类具有助益，但未来应更多关注不同法律之间的差异。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
