## 3 Related Studies
### Hate Speech Detection in Arabic Languages
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Techniques** & **Classifier** & **Language** & **Datasets** & **Preprocessing** & **Max-score** \\ \hline
[43] & 2021 & Deep Learning & CNN, CNNLSTM, BLSTM-CNN & Arabic & Collection of 23,678 & Removing (dicactics, punctuation, non-Arabic characters, emotions, and stop words). Normalizing letters & BLSTM = binary label: 80\% \\ \hline
[21] & 2021 & Deep Learning/Machine Learning & CNN, BERT & & & & \\  & Learning/Machine Learning & ALBERT, RoBERTa, LR & English & Collection of 3843 & Removing special characters, numbers, newlines, mention tags & (CNN = BERT-base-uncased + ALBERT-xlarge=\(\gamma\)) = 85.5\% Acc, \\ \hline
[23] & 2021 & Deep Learning & LR, SVM & & & \\  & Learning/Machine Learning & CNN, LSTM BLSTM & Spanish & & & \\  & Learning & XLM, mBERT BETO & & & & \\  & & & & & & \\ \hline
[44] & 2022 & Deep Learning & RNN & Arabic & Collection of 4203 & Data cleaning, symbol removal labeling, tokenization & 95.38\% Multi-label 84.14 \\ \hline
[45] & 2022 & Deep Learning & CNN, CNN-LSTM, BLSTM & Arabic & Collection of 15,060 & Data cleaning, symbol removal labeling, tokenization & CNN-LSTM = 87.27\% 88.65\% F1, 83.89\% F8.436\% R \\ \hline
[47] & 2022 & Machine Learning & GA-SVM & Arabic & Collection of 4505 & Removing non-Arabic characters, tweets & SVM = 88\% Acc. \\ \hline
[19] & 2022 & Deep Learning & CNN, LSTMs, LSTM, GRU, and CNN-LSTM & & & \\ \hline
[20] & 2022 & Deep Learning/Machine Learning & CNN = GRU & & & \\  & Learning/Machine Learning & BERT & Turkish & & & \\  & Learning & HAN & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1. Cont. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref. of Study Used** & **Dataset** & **Source** & **Region** & **Size** & **Labels** & **Year** & **Ref. of Dataset** \\ \hline
[29,41] & Religious hate speech dataset & X & Multinational & 6136 Tweets &
\begin{tabular}{c} Hate (2762), \\ not hate (3374) \\ \end{tabular} & 2019 & [29] \\ \hline
[29,43] & \
\begin{tabular}{c} L-HSAB \\ Hate speech and offensive language \\ \end{tabular} & X & Levantine & 5846 Tweets &
\begin{tabular}{c} Normal (4650), \\ offensive (1728), \\ hate (468) \\ \end{tabular} & 2019 & [31] \\ \hline
[33] & \
\begin{tabular}{c} T-HSAB \\ Hate speech and offensive language \\ \end{tabular} & X & Tunisian & 6075 Tweets &
\begin{tabular}{c} Normal (3834), \\ offensive (1127), \\ hate (1078) \\ \end{tabular} & 2019 & [33] \\ \hline
[35,28,-40,43] & \
\begin{tabular}{c} OSACT4 \\ Hate speech and offensive language \\ \end{tabular} & X & Multinational & 20,000 Tweets &
\begin{tabular}{c} Offensive (900), \\ not offensive (8100), \\ hate (50), \\ not hate (9950) \\ \end{tabular} & 2020 & [35] \\ \hline
[45] & \
\begin{tabular}{c} Offensive language \\ \end{tabular} & YouTube & Multinational & 14,202 Comments &
\begin{tabular}{c} Offensive (9549), \\ offensive (4853) \\ \end{tabular} & 2018 & [45] \\ \hline
[47] & \
\begin{tabular}{c} Arcybc \\ Offensive language, cyberbullying \\ \end{tabular} & X & Multinational & 4505 Tweets &
\begin{tabular}{c} Offensive (1887), \\ cyberbullying (1728), \\ Normal (890) \\ \end{tabular} & 2022 & [47] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Publicly available Arabic language datasets.