  The automatic detection of hate speech online is an active research area in
NLP. Most of the studies to date are based on social media datasets that
contribute to the creation of hate speech detection models trained on them.
However, data creation processes contain their own biases, and models
inherently learn from these dataset-specific biases. In this paper, we perform
a large-scale cross-dataset comparison where we fine-tune language models on
different hate speech detection datasets. This analysis shows how some datasets
are more generalisable than others when used as training data. Crucially, our
experiments show how combining hate speech detection datasets can contribute to
the development of robust hate speech detection models. This robustness holds
even when controlling by data size and compared with the best individual
datasets.
