Harnessing the Power of Interdisciplinary Research with Psychology-Informed Cyberbullying Detection Models

Deborah Hall, Yasin N. Silva, Brittany Wheeler, Lu Cheng, Katie Baumel

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalInternational Journal of Bullying Prevention
DOIs
StateAccepted/In press - 2021

Keywords

  • Computer science
  • Cyberbullying
  • Interdisciplinary research
  • Machine learning
  • Psychology
  • Social media

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Social Psychology
  • Social Sciences (miscellaneous)

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