LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation
- URL: http://arxiv.org/abs/2503.07237v1
- Date: Mon, 10 Mar 2025 12:20:20 GMT
- Title: LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation
- Abstract summary: Non-native moderators struggle with interpreting culturally-specific knowledge, sentiment, and internet culture in hate speech moderation.<n>Our system achieves 78% accuracy, while reducing human workload by 83.6%.<n>Our findings suggest that non-native moderators, when properly supported by LLMs, can effectively contribute to cross-cultural hate speech moderation.
- Score: 13.018071368511507
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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