Document Type

Article

Publication Date

2022

Publication Title

International Journal of Bullying Prevention

Volume

4

Issue

1

Pages

47-54

Publisher Name

Springer

Abstract

Cyberbullying has become increasingly prevalent, particularly on social media. There has also been a steady rise in cyberbullying research across a range of disciplines. Much of the empirical work from computer science has focused on developing machine learning models for cyberbullying detection. Whereas machine learning cyberbullying detection models can be improved by drawing on psychological theories and perspectives, there is also tremendous potential for machine learning models to contribute to a better understanding of psychological aspects of cyberbullying. In this paper, we discuss how machine learning models can yield novel insights about the nature and defining characteristics of cyberbullying and how machine learning approaches can be applied to help clinicians, families, and communities reduce cyberbullying. Specifically, we discuss the potential for machine learning models to shed light on the repetitive nature of cyberbullying, the imbalance of power between cyberbullies and their victims, and causal mechanisms that give rise to cyberbullying. We orient our discussion on emerging and future research directions, as well as the practical implications of machine learning cyberbullying detection models.

Comments

Author Posting © The Authors, 2021. This is the authors' version of the work. It is posted here by permission of the Sage Publishing for personal use, not for redistribution. The definitive version was published in the International Journal of Bullying Prevention, Volume 4, Pages 47-54, 2022. https://doi.org/10.1007/s42380-021-00107-5

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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