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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mouaad Errami
Hassan II University of Casablanca

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Classifying toxicity in the Arabic Moroccan dialect on Instagram: a machine and deep learning approach Rabia Rachidi; Mohamed Amine Ouassil; Mouaad Errami; Bouchaib Cherradi; Soufiane Hamida; Hassan Silkan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp588-598

Abstract

People crave interaction and connection with other people. Therefore, social media became the center of society’s life. Among the brightest social media platforms nowadays with a massive number of daily users there is Instagram, which is due to its distinctive features. The excessive revealing of personal life has put users in the spots of getting bullied and harassed and getting toxic revues from other users. Numerous studies have targeted social media to fight its harmful side effects. Nevertheless, most of the datasets that were already available were in English, the Arabic Moroccan dialect ones were not. In this work, the Arabic Moroccan dialect dataset has been extracted from the Instagram platform. Furthermore, feature extraction techniques have been applied to the collected dataset to increase classification accuracy. Afterward, we developed models using machine learning and deep learning algorithms to detect and classify toxicity. For the models’ evaluation, we have used the most used metrics: accuracy, precision, F1-score, and recall. The experimental results gave modest scores of around 70% to 83%. These results imply that the models need improvement due to the lack of available datasets and the preprocessing libraries to handle the Moroccan dialect of Arabic.
Co-Authors Bouchaib Cherradi Hassan Silkan Mohamed Amine Ouassil Rabia Rachidi Soufiane Hamida