Untangling Hate Speech Definitions: A Semantic Componential Analysis Across Cultures and Domains
This work addresses the challenge of inconsistent hate speech definitions for researchers and practitioners in AI and social sciences, but it is incremental as it builds on existing analysis methods with new data.
The study tackled the problem of varying hate speech definitions across cultures and domains by creating a dataset of 493 definitions from over 100 cultures and analyzing them using a Semantic Componential Analysis framework, finding that LLMs' hate speech detection responses change based on definition complexity in prompts.
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.