@inproceedings{sani-etal-2025-wrote,
    title = "Who Wrote This? Identifying Machine vs Human-Generated Text in {H}ausa",
    author = "Sani, Babangida  and
      Soy, Aakansha  and
      Imam, Sukairaj Hafiz  and
      Mustapha, Ahmad  and
      Aliyu, Lukman Jibril  and
      Abdulmumin, Idris  and
      Ahmad, Ibrahim Said  and
      Muhammad, Shamsuddeen Hassan",
    editor = "Lignos, Constantine  and
      Abdulmumin, Idris  and
      Adelani, David",
    booktitle = "Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.africanlp-1.12/",
    pages = "82--88",
    ISBN = "979-8-89176-257-2",
    abstract = "The advancement of large language models (LLMs) has allowed them to be proficient in various tasks, including content generation. However, their unregulated usage can lead to malicious activities such as plagiarism and generating and spreading fake news, especially for low-resource languages. Most existing machine-generated text detectors are trained on high-resource languages like English, French, etc. In this study, we developed the first large-scale detector that can distinguish between human- and machine-generated content in Hausa. We scraped seven Hausa-language media outlets for the human-generated text and the Gemini-2.0 flash model to automatically generate the corresponding Hausa-language articles based on the human-generated article headlines. We fine-tuned four pre-trained African-centric models (AfriTeVa, AfriBERTa, AfroX LMR, and AfroXLMR-76L) on the resulting dataset and assessed their performance using accuracy and F1-score metrics. AfroXLMR achieved the highest performance with an accuracy of 99.23{\%} and an F1 score of 99.21{\%}, demonstrating its effectiveness for Hausa text detection. Our dataset is made publicly available to enable further research."
}