## 3 Related Studies
### Hate Speech Detection in Arabic Languages  
This section focuses on studies conducted specifically on Arabic hate speech detection, providing a detailed overview of the methods, tools and models used. [PERSON] et al. [29] compiled a dataset of 6000 Arabic tweets containing religious hate speech, categorizing each tweet into hate or non-hate categories and indicating specific religions for hate tweets [30]. During preprocessing, diacritical marks, punctuation, emojis, non-Arabic characters and single-letter words were removed and normalized. The dataset was analyzed using machine learning models, including logistic regression (LR), support vector machine (SVM) and gated recurrent units (GRU), with the GRU-based model achieving an F1 score of 77%. [PERSON] et al. [31] presented the L-HSAB dataset [32], comprising 5846 tweets labeled as normal, offensive, or hateful, with naive Bayes (NB) achieving the highest F1 score of 74.4%. [PERSON] et al. [33] developed the T-HSAB dataset for Tunisian Arabic [34], containing 6075 comments divided into normal (3834), offensive (1127), and hate (1078) categories, with NB outperforming SVM, achieving an F1 score of 83.6%. [PERSON] et al. [35] used the OSACT4 dataset [36] to identify offensive speech in Arabic, integrating convolutional neural networks (CNN) and bidirectional gated recurrent units (Bi-GRU) enhanced with attention layers, achieving an F1 score of 85%. [PERSON] et al. [37] collected data from multiple OSNs and evaluated 12 machine learning techniques and two deep learning models, with recurrent neural networks (RNN) achieving an F1 score of 98.7%, despite challenges in reproducibility due to limited transparency. [PERSON] [38] focused on preprocessing informal social media texts for offensive language detection using the OSACT4 dataset and an SVM model, achieving 90% accuracy, although this was limited by the absence of advanced features during model training. [PERSON] et al. [39] utilized the OSACT4 dataset of 10,000 tweets, with long short-term Memory (LSTM) performing best, with an F1 score of 85%. [PERSON] et al. [40] used AraBERT with multitasking and multilabel classification techniques on the OSACT4 dataset, achieving a macro-F1 score of 90.15%, although it faced misclassification issues in non-offensive contexts. [PERSON] et al. [41] developed a dataset of 9316 annotated tweets, with a CNN model achieving an F1 score of 79%, although the study's results were context-specific. [PERSON] et al. [42] achieved a 91% hit rate using a dataset of 3696 tweets processed with term frequency-inverse document frequency (TF-IDF) and bag of words (BoW) methods, but faced challenges with mislabeled data. [PERSON] et al. [43] combined multiple datasets (ArHS, L-HSAB, and OSACT4) for a total of 23,678 tweets, using BiLSTM-CNN to achieve F1 scores of 79%, 74%, and 73% for binary, ternary, and multi-label classifications, respectively. [PERSON] [44] developed a dataset of 4203 comments categorized into seven classes, achieving impressive results with deep recurrent neural networks: 99.73% accuracy for binary classification, 95.38% for ternary classification and 84.14% for multi-class classification, although limited by its small dataset size. [PERSON] et al. [45] used a dataset of 15,050 YouTube comments [46], with the CNN-LSTM model achieving an F1 score of 83.65%, but noted the dataset's outdated nature. [PERSON] et al. [47] developed a two-stage system using pre-trained word embeddings and a hybrid classification approach evaluated on the ArCybC dataset [48], with the SVM classifier achieving an F1 score of 87.8%, although it was affected by the outdated dataset [49]. Collectively, these studies underscore the importance of tailored approaches and highlight the challenges in Arabic hate speech detection, particularly concerning dataset diversity, dialect variation and evolving linguistic trends. The techniques discussed are summarized in Table 1, while Table 2 shows the only publicly available Arabic datasets used in hate speech detection studies.