Research Output
Building Towards Automated Cyberbullying Detection: A Comparative Analysis
  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.

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    25 June 2022

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  • Funders:

    Edinburgh Napier Funded


Al Harigy, L. M., Al Nuaim, H. A., Moradpoor, N., & Tan, Z. (2022). Building Towards Automated Cyberbullying Detection: A Comparative Analysis. Computational Intelligence and Neuroscience, 2022, Article 4794227.


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