@inproceedings{dehghan-yanikoglu-2024-evaluating,
    title = "Evaluating {C}hat{GPT}{'}s Ability to Detect Hate Speech in {T}urkish Tweets",
    author = "Dehghan, Somaiyeh  and
      Yanikoglu, Berrin",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Tanev, Hristo  and
      Thapa, Surendrabikram  and
      Uludo{\u{g}}an, G{\"o}k{\c{c}}e},
    booktitle = "Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.case-1.6/",
    pages = "54--59",
    abstract = "ChatGPT, developed by OpenAI, has made a significant impact on the world, mainly on how people interact with technology. In this study, we evaluate ChatGPT{'}s ability to detect hate speech in Turkish tweets and measure its strength using zero- and few-shot paradigms and compare the results to the supervised fine-tuning BERT model. On evaluations with the SIU2023-NST dataset, ChatGPT achieved 65.81{\%} accuracy in detecting hate speech for the few-shot setting, while BERT with supervised fine-tuning achieved 82.22{\%} accuracy. This results supports previous findings that show that, despite its much smaller size, BERT is more suitable for natural language classifications tasks such as hate speech detection."
}