# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 2 Related Work  
Abusive language in social media is a complex phenomenon with a wide spectrum of overlapping forms and targets. Hate speech, offensive language, and cyberbullying are examples of abusiveness, and several works in the literature have been conducted to detect and locate such types of language. Recently, researchers have shown an increased interest in automatic hate speech detection in social media. Most of the existing studies in the literature have primarily modeled the problem as a supervised classification task, whether using classical machine learning approaches or deep neural network approaches [2, 6]. **Machine learning approaches:** As for classical machine learning approaches, there are several studies that leveraged simple surface and linguistic features such as bag of words (BOW), n-grams, and POS(part-of-speech) as fundamental features. [PERSON] and [PERSON] [2] presented a review of the various features that were used for the task in the literature. One of the earliest studies that addressed the problem of hate speech detection was presented by [15], which focused on detecting anti-Semitic language as the specific type of hate speech. [PERSON] and [PERSON] [16] proposed a supervised machine learning model to detect racist hate speech against black people in Twittersphere and they suggested improving the model by considering other features such as sentiment features and bigrams. [PERSON] and [PERSON] [3] presented a study to detect hate speech in the Twitter platform, specifically targeting two types of hate: racism and sexism. They investigated several features including character n-gram along with demographic, lexical, and geographic users' features. The results showed that using character n-grams of lengths up to four provided the best results, and incorporating gender as an additional feature could yield slight improvement. They also built and published a dataset of 16,000 annotated tweets that has become a well-known benchmarking dataset for following studies. Word embeddings as features were also investigated by [PERSON] et al. [17]. They used paragraph2 vec to learn low-dimensional text embeddings to classify users' comments as abusive or clean. Compared with BOW representations, the experimental results on Yahoo! Finance user comments showed that paragraph2 vec representation performed better in term of area under the curve (AUC). Following this study, [PERSON] et al. [18] incorporated various features such as n-grams, linguistic and syntactic features, and distributional semantics in order to detect abusive language in online user comments. The results on Yahoo! Finance user comments showed that combining all features led to outperformance of the prior art in terms of AUC. [PERSON] et al. [4] trained a multi-class classifier to distinguish between offensive and hate language in tweets. They also built and published a dataset of tweets labelled for three categories: hate speech, offensive language, or neither, resulting into 24,802 labelled tweets. There are also several works that tackle the problem of hate speech in other languages using classical machine learning methods. [PERSON] and [PERSON] [5] targeted the hate speech occurring in right-wing German tweets. [PERSON] et al. [19] experimented with several features to detect hate speech in Hindi-English code-mixed tweets. [PERSON] et al. [20] presented a study that tackles the problem of online Jihadist hate speech written in many languages in Twitter. **Deep learning approaches:** In recent years, attention has shifted toward deep learning approaches to tackle the problem of hate speech detection. Most of the works have employed various architectures of convolutional neural network (CNN) and recurrent neural networks (RNN). [PERSON] and [PERSON] [7] proposed a two-step classification approach by combining two classifiers: one to classify the text to abusive or not, and another to classify the text into a specific type of abusive language (sexist or racist) given that the text is abusive.