## 3 Related Studies
### Hate Speech Detection in Non-Arabic Languages  
Several studies have explored hate speech detection in different languages using advanced machine learning and deep learning techniques. [PERSON] [19] focused on Afaan Oromo, leveraging tweets and Facebook posts annotated by linguistic experts and testing models such as CNNs, LSTMs, BiLSTMs, GRUs, and CNN-LSTM hybrids, with BiLSTM achieving the highest F1 score of 91%. This study highlighted the benefits of pre-trained embeddings and extended sampling, suggesting broader applicability across languages. Similarly, reference [20] examined Turkish hate speech detection using print media articles, employing models including the Hierarchical Attention Network (HAN) and BERT. BERT achieved the best accuracy of 90.6% when augmented with Turkish-specific linguistic features, underlining its robustness.  
For the English language, [PERSON] et al. [21] utilized neural networks on the HASOC-2021 dataset, comparing BERT-based models, CNNs and ensembles, with the ensemble classifier achieving an F1 score of 85.1%, despite minor misdetections. [PERSON] et al. [22] addressed hate speech in Hinglish, using context-based embeddings such as ELMo, FLAIR, and BERT, achieving an F1 score of 87%, demonstrating their effectiveness for code-mixed languages. [PERSON] et al. [23] analyzed Spanish hate speech using datasets from HaterNet and SemEval 2019, testing models such as BERT, XLM, and BETO, with F1 scores of 65.8% and 75.5%, respectively.  
These findings emphasized the challenges posed by rare words and mislabeling, underscoring the need for improved datasets and model robustness. These studies collectively showcase the potential of advanced models and tailored approaches in addressing hate speech across diverse languages and contexts.  
As with other models, research [24; 25] has concluded that AraBERT is created to focus on the Arabic language. It is fine-tuned on massive amounts of Arabic text data, bringing in the ability to understand the finer aspects of the Arabic language, including its morphology and syntax. As has been demonstrated, AraBERT outperforms Arabic NLP tasks [26], such as sentiment analysis, named entity recognition and question answering. As a result, this model is a substantial breakthrough in the field of Arabic NLP, as it overcomes the problem of limited access to high-quality Arabic NLP sources and models.  
mBERT [27] is expected to deal with several languages at any given time. The model is a variant of BERT and is initialized with a corpus of 104 languages, thus supporting transfer learning. This enables the model to transfer knowledge from one language to another, which enhances performance from one language to the other, and mBERT is useful in multilingual tasks, such as multilingual translation, cross-lingual opinion mining and multilingual question and answer. This novel model alleviates the lack of connection between NLP studies in one language and another, allowing for scholarly unity and cooperative effort.  
DistilBERT is one of the variants of BERT [28], which intends to minimize memory consumption and computational time. It is more compact than the 'classic' BERT, it outperforms sacrificing mere parameter count, and has substantially faster inference times. DistilBERT minimizes redundancy and is trained to match outcomes of a larger, teacher model (such as BERT), using knowledge distillation methods. In its current form, this model provides a feasible approach to incorporating BERT-like models to contexts where resources are limited or to situations where responses must be generated as quickly as possible.