Comparative Study of Machine Learning Models for the Detection of Abusive Messages: Case of Wolof-French Codes Mixing Data
Language
EN
Communication dans un congrès
This item was published in
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
English 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 ...Read more >
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.Read less <