The internet has become a hotspot for hate speech (HS), threatening societal harmony and individual well-being. While automatic detection methods perform well in identifying explicit hate speech (ex-HS), they struggle with more subtle forms, such as implicit hate speech (im-HS). We tackle this problem by introducing a new taxonomy for im-HS detection, defining six encoding strategies named codetypes. We present two methods for integrating codetypes into im-HS detection: 1) prompting large language models (LLMs) directly to classify sentences based on generated responses, and 2) using LLMs as encoders with codetypes embedded during the encoding process. Experiments show that the use of codetypes improves im-HS detection in both Chinese and English datasets, validating the effectiveness of our approach across different languages.
@article{arxiv.2506.04693,
title = {Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification},
author = {Lu Wei and Liangzhi Li and Tong Xiang and Xiao Liu and Noa Garcia},
journal= {arXiv preprint arXiv:2506.04693},
year = {2025}
}
Comments
Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025), 112-126