Detection of Arabic offensive language in social media using machine learning models

This research aims to detect different types of Arabic offensive language in twitter. It uses a multiclass classification system in which each tweet is categorized into one or more of the offensive language types based on the used word(s). In this study, five types are classified, which are: bullying, insult, racism, obscene, and non-offensive. To classify the abusive language, a cascaded model consisting of Bidirectional Encoder Representation of Transformers (BERT) models (AraBERT, ArabicBERT, XLMRoBERTa, GigaBERT, MBERT, and QARiB), deep learning models (1D-CNN, BiLSTM), and Radial Basis Function (RBF) is presented in this work. In addition, various types of machine learning models are utilized. The dataset is collected from twitter in which each class has the same number of tweets (balanced dataset). Each tweet is assigned to one or more of the selected offensive language types to build multiclass and multilabel systems. In addition, a binary dataset is constructed by assigning the tweets to offensive or non-offensive classes. The highest results are obtained from implementing the cascaded model started by ArabicBERT followed by BiLSTM and RBF with an accuracy, precision, recall, and F1-score of 98.4%, 98.2%,92.8%, and 98.4%, respectively. RBF records the highest results among the utilized traditional classifiers with an accuracy, precision, recall, and F1-score of 60% for each measurement individually, while KNN records the lowest results obtaining 45%, 46%, 45%, and 43% in terms of accuracy, precision, recall, and F1-score, respectively.