厘清仇恨言论定义：跨文化与跨领域的语义成分分析
Untangling Hate Speech Definitions: A Semantic Componential Analysis Across Cultures and Domains
AI总结：
针对仇恨言论定义受文化影响、解读不一的问题，提出语义成分分析框架，构建含493条跨文化跨领域定义的首个数据集，实验发现开源LLM的仇恨言论检测结果对定义复杂度敏感。
摘要：
仇恨言论的界定受文化影响极大，导致个体解读存在差异。为此，我们提出语义成分分析（Semantic Componential Analysis, SCA）框架，用于对仇恨言论定义开展跨文化与跨领域分析。我们构建了首个仇恨言论定义数据集，涵盖来自100余种文化的493条定义，这些定义取自五大核心领域：在线词典、学术研究、Wikipedia、法律文本和在线平台。通过将这些定义分解为语义成分，我们的分析显示不同定义间存在显著差异，但许多领域在相互借鉴定义时并未考虑目标文化的情况。我们利用所构建的数据集开展零样本模型实验，采用三款主流开源大语言模型（LLM），以探究不同定义对仇恨言论检测的影响。研究结果表明，LLM对定义具有敏感性：仇恨言论检测的结果会随提示词中所用定义的复杂程度而变化。
英文摘要：
Hate speech relies heavily on cultural influences, leading to varying individual interpretations. For that reason, we propose a Semantic Componential Analysis (SCA) framework for a cross-cultural and cross-domain analysis of hate speech definitions. We create the first dataset of hate speech definitions encompassing 493 definitions from more than 100 cultures, drawn from five key domains: online dictionaries, academic research, Wikipedia, legal texts, and online platforms. By decomposing these definitions into semantic components, our analysis reveals significant variation across definitions, yet many domains borrow definitions from one another without taking into account the target culture. We conduct zero-shot model experiments using our proposed dataset, employing three popular open-sourced LLMs to understand the impact of different definitions on hate speech detection. Our findings indicate that LLMs are sensitive to definitions: responses for hate speech detection change according to the complexity of definitions used in the prompt.
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。
