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