  The performance of hate speech detection models relies on the datasets on
which the models are trained. Existing datasets are mostly prepared with a
limited number of instances or hate domains that define hate topics. This
hinders large-scale analysis and transfer learning with respect to hate
domains. In this study, we construct large-scale tweet datasets for hate speech
detection in English and a low-resource language, Turkish, consisting of
human-labeled 100k tweets per each. Our datasets are designed to have equal
number of tweets distributed over five domains. The experimental results
supported by statistical tests show that Transformer-based language models
outperform conventional bag-of-words and neural models by at least 5% in
English and 10% in Turkish for large-scale hate speech detection. The
performance is also scalable to different training sizes, such that 98% of
performance in English, and 97% in Turkish, are recovered when 20% of training
instances are used. We further examine the generalization ability of
cross-domain transfer among hate domains. We show that 96% of the performance
of a target domain in average is recovered by other domains for English, and
92% for Turkish. Gender and religion are more successful to generalize to other
domains, while sports fail most.
