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This research investigates the efficacy of cross-lingual transfer for hate speech detection across 14 languages, identifying linguistic similarity as the primary driver of performance consistency. The study highlights that while measurable factors like dataset size play a secondary role, shared cultural norms and communicative patterns significantly influence transfer success in low-resource scenarios. These findings provide critical guidance for building more robust multilingual AI safety systems by accounting for both linguistic and cultural variables.
When Does Hate Transfer? A Large-Scale Study of Cross-Lingual Hate Speech Detection
arXiv:2610.27285v1 Announce Type: new Abstract: Effective detection of hate content is essential for maintaining healthy online communities, yet performance can vary substantially across languages. This work investigates a core practical question: Can training data from one language support hate-speech detection in another language with comparable performance? To answer this question, we conduct a large-scale study across 14 languages. \change{We observe consistent patterns in which certain language pairs transfer more effectively than others. Among the factors we measure, linguistic similarity is the strongest predictor of these patterns, with temporal overlap and dataset-size difference playing smaller roles. These measurable factors do not fully explain the observed patterns; a complementary qualitative analysis suggests that shared cultural cues and communicative norms may be associated with some of the remaining cases, particularly within several clusters. We present this cultural interpretation as a hypothesis from qualitative inspection rather than a measured cause.}{} Our analysis includes low-resource languages, offering practical insights into when cross-lingual transfer can support them and where its limitations emerge. These findings inform the design of more reliable multilingual hate-detection systems.
Title:When Does Hate Transfer? A Large-Scale Study of Cross-Lingual Hate Speech Detection
Abstract:Effective detection of hate content is essential for maintaining healthy online communities, yet performance can vary substantially across languages. This work investigates a core practical question: Can training data from one language support hate-speech detection in another language with comparable performance? To answer this question, we conduct a large-scale study across 14 languages. \change{We observe consistent patterns in which certain language pairs transfer more effectively than others. Among the factors we measure, linguistic similarity is the strongest predictor of these patterns, with temporal overlap and dataset-size difference playing smaller roles. These measurable factors do not fully explain the observed patterns; a complementary qualitative analysis suggests that shared cultural cues and communicative norms may be associated with some of the remaining cases, particularly within several clusters. We present this cultural interpretation as a hypothesis from qualitative inspection rather than a measured cause.}{} Our analysis includes low-resource languages, offering practical insights into when cross-lingual transfer can support them and where its limitations emerge. These findings inform the design of more reliable multilingual hate-detection systems.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.27285 [cs.CL]
(or arXiv:2610.27285v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.27285 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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