A Review on Deep-Learning-Based Cyberbullying Detection

Md. Tarek Hasan, Md. Al Emran Hossain, Md. Saddam Hossain Mukta, Arifa Akter, Mohiuddin Ahmed and Salekul Islam ()
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Md. Tarek Hasan: Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
Md. Al Emran Hossain: Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
Md. Saddam Hossain Mukta: Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
Arifa Akter: Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
Mohiuddin Ahmed: School of Science, Edith Cowan University, Joondalup 6027, Australia
Salekul Islam: Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh

Future Internet, 2023, vol. 15, issue 5, 1-47

Abstract: Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented.

Keywords: cyberbullying; machine learning; data representations; deep learning; frameworks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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