Is it Offensive or Abusive? : An Empirical Study of Hateful Language Detection of Arabic Social Media Texts?

Among many potential subjects studied in Sentiment Analysis, widespread offensive and abusive language on social media has triggered interest in reducing its risks on users; children in particular. This paper centres on distinguishing between offensive and abusive language detection within Arabic social media texts through the employment of various machine and deep learning techniques. The techniques include Naïve Bayes (NB), Support Vector Machine (SVM), fastText, keras, and RoBERTa XML multilingual embeddings, which have demonstrated superior performance compared to other statistical machine learning methods and different kinds of embeddings like fastText. The methods were implemented on two separate corpora from YouTube comments totalling 47K comments. The results demonstrated that all models, except NB, reached an accuracy of 82%. It was also shown that word tri-grams enhance classification performance, though other tuning techniques were applied such as TF-IDF and grid-search. The linguistic findings, aimed at distinguishing between offensive and abusive language, were consistent with machine learning (ML) performance, which effectively classified the two distinct classes of sentiment: offensive and abusive.