  Deep neural networks have been adopted successfully in hate speech detection
problems. Nevertheless, the effect of the word embedding models on the neural
network's performance has not been appropriately examined in the literature. In
our study, through different detection tasks, 2-class, 3-class, and 6-class
classification, we investigate the impact of both word embedding models and
neural network architectures on the predictive accuracy. Our focus is on the
Arabic language. We first train several word embedding models on a large-scale
unlabelled Arabic text corpus. Next, based on a dataset of Arabic hate and
offensive speech, for each detection task, we train several neural network
classifiers using the pre-trained word embedding models. This task yields a
large number of various learned models, which allows conducting an exhaustive
comparison. The empirical analysis demonstrates, on the one hand, the
superiority of the skip-gram models and, on the other hand, the superiority of
the CNN network across the three detection tasks.
