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Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Daniyar Sultan1,2, Aigerim Toktarova3,*, Ainur Zhumadillayeva4, Sapargali Aldeshov5,6, Shynar Mussiraliyeva1, Gulbakhram Beissenova6,7, Abay Tursynbayev8, Gulmira Baenova4, Aigul Imanbayeva6
1 Al-Farabi Kazakh National University, Almaty, Kazakhstan
2 International Information Technology University, Almaty, Kazakhstan
3 Akhmet Yassawi International Kazakh-Turkish University, Turkistan, Kazakhstan
4 L.N.Gumilyov Eurasian National University, Astana, Kazakhstan
5 South Kazakhstan State Pedagogical University, Shymkent, Kazakhstan
6 M. Auezov South Kazakhstan University, Shymkent, Kazakhstan
7 University of Friendship of People’s Academician A. Kuatbekov, Shymkent, Kazakhstan
8 National Academy of Education named after Y. Altynsarin, Astana, Kazakhstan
* Corresponding Author: Aigerim Toktarova. Email:

Computers, Materials & Continua 2023, 74(1), 2115-2131. https://doi.org/10.32604/cmc.2023.032993

Received 03 June 2022; Accepted 12 July 2022; Issue published 22 September 2022

Abstract

Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying. However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem. In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem. We deployed three deep and six shallow learning algorithms for cyberbullying detection problems. The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.

Keywords

Cyberbullying; machine learning; deep learning; classification; NLP

Cite This Article

D. Sultan, A. Toktarova, A. Zhumadillayeva, S. Aldeshov, S. Mussiraliyeva et al., "Cyberbullying-related hate speech detection using shallow-to-deep learning," Computers, Materials & Continua, vol. 74, no.1, pp. 2115–2131, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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