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.

翻译：当前，阿拉伯语推文中的仇恨言论分类已引起多位研究者的关注。为解决此分类任务，已有多种系统与技术被开发出来。然而，在此背景下仍面临两大主要挑战：性能有限与数据不平衡问题。本研究提出一种新颖方法，该方法基于先前人工标注数据，结合集成学习与半监督学习技术。我们通过在基准数据集上进行实验，将阿拉伯语推文分类为五个独立类别：非仇恨言论、一般仇恨言论、种族歧视、宗教歧视或性别歧视。实验结果表明：（1）基于预训练语言模型的集成学习方法优于现有相关研究；（2）我们提出的数据增强方法提升了阿拉伯语推文仇恨言论检测的准确率，并超越了现有相关研究。我们的主要贡献在于取得了具有启发性的阿拉伯语仇恨言论检测结果。