  Today, hate speech classification from Arabic tweets has drawn the attention
of several researchers. Many systems and techniques have been developed to
resolve this classification task. Nevertheless, two of the major challenges
faced in this context are the limited performance and the problem of imbalanced
data. In this study, we propose a novel approach that leverages ensemble
learning and semi-supervised learning based on previously manually labeled. We
conducted experiments on a benchmark dataset by classifying Arabic tweets into
5 distinct classes: non-hate, general hate, racial, religious, or sexism.
Experimental results show that: (1) ensemble learning based on pre-trained
language models outperforms existing related works; (2) Our proposed data
augmentation improves the accuracy results of hate speech detection from Arabic
tweets and outperforms existing related works. Our main contribution is the
achievement of encouraging results in Arabic hate speech detection.
