@inproceedings{al-mandhari-etal-2024-offensive,
    title = "Is it Offensive or Abusive? An Empirical Study of Hateful Language Detection of {A}rabic Social Media Texts",
    author = "Al Mandhari, Salim  and
      El-Haj, Mo  and
      Rayson, Paul",
    editor = "Mitkov, Ruslan  and
      Ezzini, Saad  and
      Ranasinghe, Tharindu  and
      Ezeani, Ignatius  and
      Khallaf, Nouran  and
      Acarturk, Cengiz  and
      Bradbury, Matthew  and
      El-Haj, Mo  and
      Rayson, Paul",
    booktitle = "Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    month = jul,
    year = "2024",
    address = "Lancaster, UK",
    publisher = "International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.nlpaics-1.16/",
    pages = "137--146",
    abstract = {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 detec- tion within Arabic social media texts through the employment of various machine and deep learning techniques. The techniques include Na{\"i}ve Bayes (NB), Support Vector Machine (SVM), fastText, keras, and RoBERTa XML multilingual embeddings, which have demon- strated superior performance compared to other statistical machine learning methods and dif- ferent 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 en- hance classification performance, though other tuning techniques were applied such as TF-IDF and grid-search. The linguistic findings, aimed at distinguishing between offensive and abu- sive language, were consistent with machine learning (ML) performance, which effectively classified the two distinct classes of sentiment: offensive and abusive.}
}