NDAO, I.; DRAMÉ, K.; SAMBE, G.; DIALLO, Gayo

doi:10.1007/978-3-031-86493-3_20

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Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3–4, 2024, Proceedings, EAI INTERSOL 2024 - 7th EAI International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas, 2024-07-03, Dakar. 2025-04-21, vol. 610, p. 252-263

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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. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.< Réduire

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

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