# Arabic Hate Speech Detection Using Deep Recurrent Neural Networks
## 2 Literature Review
The authors of [15] developed a dataset consisting of 10,000 tweets divided into the following classes: 1915 offensive tweets, of which 225 were deemed vulgar and 506 were labelled as hate speech, and the remaining 8085 tweets that were deemed clean. They used various classifiers, such as AdaBoost, Gaussian NB, perceptron, gradient boosting, logistic regression (LR), and SVM. They reported the best result with a precision of 88.6% using SVM. In [16], the authors developed a dataset for Arabic speech collected from various sources, such as Facebook, Instagram, YouTube, and Twitter. They collected a total of20,000 posts, tweets, and comments. They tested the dataset with 12 machine learning algorithms and two deep learning algorithms. They reported that the highest accuracy of 98.7% was achieved using the RNN. In [17], the authors responded to a competition, OSACT4, to develop a machine learning algorithm for the classification of Arabic text and the detection of offensive text. They were provided with a dataset of 10,000 tweets. Their proposed method consisted of using ULMFiT, which was originally developed for English language detection. By using forward and backward training and finding the average of the two, they reported an accuracy of 96%. The authors of [18] also responded to a competition for the detection of Arabic text that was deemed ironic. The competition was held by the Forum for Information Retrieval (FIRE2019). They provided the contestants with a dataset of 4024 tweets divided into 2091 ironic and 1933 non-ironic tweets. The authors extracted several features, including TF-IDF word n-gram features, bag-of-words features, topic modeling features, and sentiment features. They used three types of ensemble learning: hybrid, deep, and classical. They reported their best F1 score of 84.4 and achieved third place. The authors of [19] collected 450,000 tweets, of which 2000 were annotated and labeled. They extracted various features from the tweets, including syntactic dependencies between terms, their claim of well-founded/justified discrimination against social groups, and incitement to respond with antagonistic action. They found that the ensemble classification approach was the best at detecting hate speech. In [20], the authors used a publicly available dataset of 15,050 comments collected from controversial YouTube videos about Arabic celebrities. After preprocessing, the authors tested the data using the following classifiers: convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), Bi-LSTM and attention mechanisms, and finally a combined CNN-LSTM architecture. They also used Bayesian optimization to tune the hyperparameters of the network models. They reported that their best result was achieved using CNN-LSTM, with a recall of 83.46%. In [21], the authors developed a dataset consisting of 3235 tweets concerning religious issues, of which 2590 were labeled as \"not hate\" and 642 were labeled as \"hate speech.\" They tested the dataset using many classifiers, including RF, complement NB, decision tree (DT), and CNN. They also employed two deep learning methods: CNN and RNN. CNN with FastText achieved the highest F1 measure of 52%. In [22], the authors used a publicly available dataset from Fox News user comments consisting of 1528 comments, of which 435 were labeled as hateful. The authors employed the use of LSTM, bidirectional LSTM, bidirectional LSTM with attention, and an ensemble model. They found that the best results were achieved using the ensemble model, with an accuracy of 77.9%. Table 1 presents a summary of these recent studies on text classification. In this study, the recent research was thoroughly analyzed for the latest feature extraction methods, such as those listed and detailed in [23; 24]. In addition, with reference to the preprocessing stage, the literature detailing the syntax and prejudice of text [25] and that addressing the clash between symbolic AI and ML was thoroughly analyzed [26].