English datasets predominantly reflect the perspectives of certain
nationalities, which can lead to cultural biases in models and datasets. This
is particularly problematic in tasks heavily influenced by subjectivity, such
as hate speech detection. To delve into how individuals from different
countries perceive hate speech, we introduce CReHate, a cross-cultural
re-annotation of the sampled SBIC dataset. This dataset includes annotations
from five distinct countries: Australia, Singapore, South Africa, the United
Kingdom, and the United States. Our thorough statistical analysis highlights
significant differences based on nationality, with only 59.4% of the samples
achieving consensus among all countries. We also introduce a culturally
sensitive hate speech classifier via transfer learning, adept at capturing
perspectives of different nationalities. These findings underscore the need to
re-evaluate certain aspects of NLP research, especially with regard to the
nuanced nature of hate speech in the English language