Abstract
(WARNING: This paper may contain some offensive words)
Over the past few years, abusive language and cyberbullying have known a great increase on social media in general. This phenomenon has encouraged efforts to propose solutions able to detect and prohibit such behavior. Most of these solutions are dedicated to English, but the ones that can handle Arabic are, to the best of our knowledge, rare. Many reasons lie behind this situation including the informality and ambiguity of the Arabic dialects, as well as the use of Arabic/Arabizi combinations. In this paper, we will use a collection of Arabic YouTube comments that are annotated as either “hateful” or “inoffensive” to compare the ability of five machine learning algorithms to perform correct classification on hateful Arabic comments. The algorithms are Logistic Regression, Naïve Bayes, Random Forests, Support Vector Machines, and Long Short-Term Memory. The performance metrics are Accuracy, F1-Score, Precision, and Recall.
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Notes
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[Accessed: 28-Feb-2021]
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https://scikit-learn.org/stable/ [Accessed: 28-Feb-2021]
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https://www.tensorflow.org/ [Accessed: 28-Feb-2021]
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https://colab.research.google.com/ [Accessed: 28-Feb-2021]
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Boulouard, Z., Ouaissa, M., Ouaissa, M. (2022). Machine Learning for Hate Speech Detection in Arabic Social Media. In: Ouaissa, M., Boulouard, Z., Ouaissa, M., Guermah, B. (eds) Computational Intelligence in Recent Communication Networks . EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-77185-0_10
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