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Volume 2
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Proceeding Paper

AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets †

by
Abdullah Y. Muaad
1,2,*,
Hanumanthappa Jayappa Davanagere
1,*,
Mugahed A. Al-antari
3,*,
J. V. Bibal Benifa
4 and
Channabasava Chola
4,*
1
Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
2
IT Department, Sana’a Community College, Sana’a 5695, Yemen
3
Department of Artificial Intelligence, Sejong University, Seoul 05006, Korea
4
Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Pala 686635, India
*
Authors to whom correspondence should be addressed.
†
Presented at the 1st International Electronic Conference on Algorithms, 27 September–10 October 2021; Available online: https://ioca2021.sciforum.net/.
Comput. Sci. Math. Forum 2022, 2(1), 15; https://doi.org/10.3390/IOCA2021-10880
Published: 19 September 2021
(This article belongs to the Proceedings of The 1st International Electronic Conference on Algorithms)
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Abstract

Twitter is one of the social media platforms that is extensively used to share public opinions. Arabic text detection system (ATDS) is a challenging computational task in the field of Natural Language Processing (NLP) using Artificial Intelligence (AI)-based techniques. The detection of misogyny in Arabic text has received a lot of attention in recent years due to the racial and verbal violence against women on social media platforms. In this paper, an Arabic text recognition approach is presented for detecting misogyny from Arabic tweets. The proposed approach is evaluated using the Arabic Levantine Twitter Dataset for Misogynistic, and it gained recognition accuracies of 90.0% and 89.0% for binary and multi-class tasks, respectively. The proposed approach seems to be useful in providing practical smart solutions for detecting Arabic misogyny on social media.
Keywords: Arabic language processing; Arabic Text Representation; misogyny detection Arabic language processing; Arabic Text Representation; misogyny detection

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MDPI and ACS Style

Muaad, A.Y.; Davanagere, H.J.; Al-antari, M.A.; Benifa, J.V.B.; Chola, C. AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets. Comput. Sci. Math. Forum 2022, 2, 15. https://doi.org/10.3390/IOCA2021-10880

AMA Style

Muaad AY, Davanagere HJ, Al-antari MA, Benifa JVB, Chola C. AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets. Computer Sciences & Mathematics Forum. 2022; 2(1):15. https://doi.org/10.3390/IOCA2021-10880

Chicago/Turabian Style

Muaad, Abdullah Y., Hanumanthappa Jayappa Davanagere, Mugahed A. Al-antari, J. V. Bibal Benifa, and Channabasava Chola. 2022. "AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets" Computer Sciences & Mathematics Forum 2, no. 1: 15. https://doi.org/10.3390/IOCA2021-10880

APA Style

Muaad, A. Y., Davanagere, H. J., Al-antari, M. A., Benifa, J. V. B., & Chola, C. (2022). AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets. Computer Sciences & Mathematics Forum, 2(1), 15. https://doi.org/10.3390/IOCA2021-10880

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