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Building Towards Automated Cyberbullying Detection: A Comparative Analysis

Al Harigy, Lulwah M.R.; Al Nuaim, Hana A.; Moradpoor, Naghmeh; Tan, Zhiyuan


Hana A. Al Nuaim


The increased use of social media between digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, it’s this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data pre-processing and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension lead us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using Self-Supervised Learning (SSL) as an annotation technique for cyberbullying detection is explored.

Journal Article Type Review
Acceptance Date May 30, 2022
Online Publication Date Jun 25, 2022
Publication Date Jun 25, 2022
Deposit Date May 31, 2022
Publicly Available Date Jun 25, 2022
Journal Computational Intelligence and Neuroscience
Print ISSN 1687-5265
Electronic ISSN 1687-5273
Publisher Hindawi
Peer Reviewed Peer Reviewed
Volume 2022
Article Number 4794227
Public URL


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