One major flaw in hate speech identification is that most hate speech datasets ignore the cultural variation within a single language. We provide CREHate, a CRoss-cultural English Hate speech dataset, in order to address this. We use a two-step process to build CREHate: two steps: 1) cross-cultural annotation, and 2) cultural post collection. Using culturally offensive terms we obtain from our survey, we gather postings from four geographically different English-speaking nations (South Africa, Australia, Singapore, and the United Kingdom) and sample them from the SBIC dataset, which primarily reflects North America. The United States and the other four nations provide annotations that are gathered to create representative labels for each nation. Our data reveals statistically significant differences in hate speech annotations between nations.Among all countries, only 56.2% of the postings in CREHate reach consensus, with a pairwise label difference rate of 26% being the highest.Qualitative investigation reveals that annotators’ personal biases on contentious issues and differing interpretations of sarcasm are the main causes of label disagreement.In conclusion, we assess large language models (LLMs) in a zero-shot scenario and demonstrate that the state-of-the-art LLMs typically exhibit superior accuracy on Anglosphere country labels in CREHate.

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