Content moderation is a global challenge, yet major tech platforms prioritize high-resource languages, leaving low-resource languages with scarce native moderators. Since effective moderation depends on understanding contextual cues, this imbalance increases the risk of improper moderation due to non-native moderators' limited cultural understanding. Through a user study, we identify that non-native moderators struggle with interpreting culturally-specific knowledge, sentiment, and internet culture in the hate speech moderation. To assist them, we present LLM-C3MOD, a human-LLM collaborative pipeline with three steps: (1) RAG-enhanced cultural context annotations; (2) initial LLM-based moderation; and (3) targeted human moderation for cases lacking LLM consensus. Evaluated on a Korean hate speech dataset with Indonesian and German participants, our system achieves 78% accuracy (surpassing GPT-4o's 71% baseline), while reducing human workload by 83.6%. Notably, human moderators excel at nuanced contents where LLMs struggle. Our findings suggest that non-native moderators, when properly supported by LLMs, can effectively contribute to cross-cultural hate speech moderation.

本研究解决了在仇恨言论管理中，由于低资源语言缺乏本土调节者导致的文化理解不足的问题。提出的LLM-C3MOD系统通过增强文化背景注释、LLM初步管理和针对性的人工管理，显著提高了分析准确性并减少了人类工作量。研究表明，得到适当支持的非母语调节者能够有效参与跨文化仇恨言论的管理。

LLM-C3MOD：人类与LLM协作的跨文化仇恨言论管理系统