Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

Boulouard, Zakaria; Ouaissa, Mariya; Ouaissa, Mariyam; Krichen, Moez; Almutiq, Mutiq; Gasmi, Karim

doi:10.3390/app122412823

Open AccessArticle

Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

by

Zakaria Boulouard

¹

,

Mariya Ouaissa

²

,

Mariyam Ouaissa

²

,

Moez Krichen

^3,4

,

Mutiq Almutiq

^5,*

and

Karim Gasmi

⁶

¹

LIM, Hassan II University of Casablanca, Casablanca 20000, Morocco

²

Department of Computer Science, Moulay Ismail University, Meknes 50050, Morocco

³

FCSIT, Al-Baha University, Al-Baha 65528, Saudi Arabia

⁴

ReDCAD Laboratory, University of Sfax, Sfax 3038, Tunisia

⁵

Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, P.O. Box 6640, Buraidah 51452, Saudi Arabia

⁶

Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf University, Sakaka 72388, Saudi Arabia

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2022, 12(24), 12823; https://doi.org/10.3390/app122412823

Submission received: 11 November 2022 / Revised: 3 December 2022 / Accepted: 12 December 2022 / Published: 14 December 2022

(This article belongs to the Special Issue Recent Trends in Natural Language Processing and Its Applications)

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Abstract

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

Keywords: deep learning; hate speech detection; natural language processing; social media analytics; text mining

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MDPI and ACS Style

Boulouard, Z.; Ouaissa, M.; Ouaissa, M.; Krichen, M.; Almutiq, M.; Gasmi, K. Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning. Appl. Sci. 2022, 12, 12823. https://doi.org/10.3390/app122412823

AMA Style

Boulouard Z, Ouaissa M, Ouaissa M, Krichen M, Almutiq M, Gasmi K. Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning. Applied Sciences. 2022; 12(24):12823. https://doi.org/10.3390/app122412823

Chicago/Turabian Style

Boulouard, Zakaria, Mariya Ouaissa, Mariyam Ouaissa, Moez Krichen, Mutiq Almutiq, and Karim Gasmi. 2022. "Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning" Applied Sciences 12, no. 24: 12823. https://doi.org/10.3390/app122412823

APA Style

Boulouard, Z., Ouaissa, M., Ouaissa, M., Krichen, M., Almutiq, M., & Gasmi, K. (2022). Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning. Applied Sciences, 12(24), 12823. https://doi.org/10.3390/app122412823

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

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