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Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3–4, 2024, Proceedings

Research Article

Comparative Study of Machine Learning Models for the Detection of Abusive Messages: Case of Wolof-French Codes Mixing Data

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-86493-3_20,
        author={Ibrahima Ndao and Khadim Dram\^{e} and Gorgoumack Sambe and Gayo Diallo},
        title={Comparative Study of Machine Learning Models for the Detection of Abusive Messages: Case of Wolof-French Codes Mixing Data},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3--4, 2024, Proceedings},
        proceedings_a={INTERSOL},
        year={2025},
        month={4},
        keywords={abusive messages hate messages code mixing machine learning deep learning language models low-resource languages},
        doi={10.1007/978-3-031-86493-3_20}
    }
    
  • Ibrahima Ndao
    Khadim Dramé
    Gorgoumack Sambe
    Gayo Diallo
    Year: 2025
    Comparative Study of Machine Learning Models for the Detection of Abusive Messages: Case of Wolof-French Codes Mixing Data
    INTERSOL
    Springer
    DOI: 10.1007/978-3-031-86493-3_20
Ibrahima Ndao1,*, Khadim Dramé1, Gorgoumack Sambe1, Gayo Diallo2
  • 1: Laboratoire d’Informatique et d’Ingénierie pour l’Innovation, Université Assane Seck de Ziguinchor
  • 2: AHead, Bordeaux Population Health - INSERM 1219 and LABRI
*Contact email: i.ndao20150570@zig.univ.sn

Abstract

This paper presents a comparative study of machine learning models for detecting abusive messages, focusing on code-mixed data in Wolof and French languages. With the increasing use of digital platforms, there has been a surge in derogatory comments, necessitating effective detection strategies. The study introduces a meticulously annotated dataset of 2022 Twitter tweets, manually classified as abusive or not. Extensive experiments are conducted with various machine learning algorithms, including deep learning, with a focus on comparing their performance on the test dataset.

Keywords
abusive messages hate messages code mixing machine learning deep learning language models low-resource languages
Published
2025-04-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-86493-3_20
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