Comparative Study of Machine Learning Models for the Detection of Abusive Messages: Case of Wolof-French Codes Mixing Data
Domain:
natural language processingRecord type:
paperdataset
Creator:
NdaDraSam
Editor:
Bor
Publisher:
CCSD
Host:
International audience
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.
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