@inproceedings{jokic-etal-2024-abusive,
    title = "Abusive Speech Detection in {S}erbian using Machine Learning",
    author = "Joki{\'c}, Danka  and
      Stankovi{\'c}, Ranka  and
      {\v{S}}andrih Todorovi{\'c}, Branislava",
    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-1.18/",
    pages = "153--163",
    abstract = "The increase in the use of abusive language on social media and virtual platforms has emphasized the importance of developing efficient hate speech detection systems. While there have been considerable advancements in creating such systems for the English language, resources are scarce for other languages, such as Serbian. This research paper explores the use of machine learning and deep learning techniques to identify abusive language in Serbian text. The authors used AbCoSER, a dataset of Serbian tweets that have been labeled as abusive or non-abusive. They evaluated various algorithms to classify tweets, and the best-performing model is based on the deep learning transformer architecture. The model attained an F1 macro score of 0.827, a figure that is commensurate with the benchmarks established for offensive speech datasets of a similar magnitude in other languages."
}