Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification
Implicit hate speech (im-HS) poses significant challenges for automated detection due to its semantic opacity and strong contextual dependency. To address this, we propose the first six-dimensional codetype encoding taxonomy, systematically modeling both semantic and rhetorical characteristics of im-HS. Crucially, we integrate this encoding strategy deeply into large language model (LLM) prompt engineering and semantic embedding—marking the first such fusion of codetype guidance with LLM-based inference. We develop a cross-lingual, prompt-driven encoding detection framework grounded in this principle. Evaluated on benchmark Chinese and English im-HS datasets, our method achieves absolute F1-score improvements of 4.2–7.8 percentage points over state-of-the-art baselines. These results robustly demonstrate the effectiveness and cross-lingual generalizability of codetype-guided LLMs for implicit hate speech identification.