# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 2 Related Work
The evaluation was conducted using a benchmark dataset provided in [3], and the two-step approach compared to the one-step approach showed a promising result. On the same benchmark dataset, [PERSON] and [PERSON] [8] proposed the use of a CNN classifier with word2 vec word vectors, and their results outperformed the prior art. [PERSON] et al. [10] built an ensemble of LSTM-based classifiers to detect hate speech content in Twitter. They experimented the proposed method with the existing dataset provided by [PERSON] and [PERSON] [3]. The results showed that the proposed approach achieved better results compared to the state-of-the-art approaches. [PERSON] et al. [11] used a CNN + GRU neural network model to detect hate speech, building their own dataset from Twitter by searching tweets related to religion and refugees in general. For evaluation, they used a total of seven public datasets including their dataset (2435 annotated tweets) and compared the performance of the proposed CNN + GRU against several state-of-the-art baselines. The results showed that the CNN + GRU model achieved the highest F1 on all datasets. [PERSON] et al. [9] investigated three different neural models for hate speech detection. The proposed models were experimented on the [PERSON] and [PERSON] dataset [3], and the results showed that embeddings learned from the LSTM model when combined with GBDT (gradient boosting decision tree) led to the best accuracy result. **Cross-domain abusive language detection** has also been a topic of interest recently, in which knowledge can be transferred between different abusive domains (hate speech, offensive, aggression, etc.). In [21], authors used nine publicly available datasets that focused on different abusive language types such as aggressive behavior, hate speech, and toxic comments. These datasets have different labels, however, the authors binarized the labels on all datasets into positive (abusive language) and negative (non-abusive language). To assess the model generalization, the authors trained a support vector machine (SVM) classifier using a training split of one dataset and then tested it on the testing splits of other datasets. Results showed, expectedly, that the performance on out-domain data declined by more than 50% of F1 score in most cases. [PERSON] et al. [22] also presented cross-domain experiments using two models: linear support vector classifier (LSVC) with unigram representation and LSTM neural model, and they concatenated a hate lexicon (HurtLex). Using four English datasets, results showed that the performance on out-domain datasets was always lower. The results also showed that LSTM performed better than LSVC, and that using HurtLex helped in boosting the recall in most cases. **Arabic studies** that address the problem of hate speech detection are still limited; however, there are several studies that address some related problems such as detecting ISIS (Islamic State of Iraq and Syria) support messages [23] and identifying vulgar, obscene, offensive, or inflammatory language [24, 25]. A recent dataset for Levantine hate speech and abusive detection was published by [PERSON] et al. [26]. This dataset contained 5846 tweets, labeled as \"hate\", \"abusive\", or \"normal\", collected from Twitter using terms of potential entities that are usually targeted by abusive/hate speech. To the best to our knowledge, there is only one study that tackled the problem of automatic Arabic hate speech detection and it was presented by [PERSON] et al. [12]. In this study, the authors proposed an RNN architecture with gated recurrent units (GRU) and pre-trained word embeddings to detect religious hate speech in Arabic tweets. For evaluation, they created a dataset containing 6000 Arabic tweets, 1000 for each of the six religious' groups (Muslims, Jews, Christians, atheists, Sunnis, and Shia). The tweets were annotated as hate or non-hate tweets. The results showed that the GRU-based RNN model outperformed other traditional classifiers with respect to all evaluations.