A machine learning and qualitative examination of cyberbullying disclosures on Twitter


  • Karla Dhungana Sainju University Ontario Institute of Technology
  • Lisa Young University Ontario Institute of Technology
  • Akosua Kuffour University Ontario Institute of Technology
  • Niti Mishra University of Toronto


Cyberbullying, Bullying roles, Social media, Twitter, Machine learning, Qualitative content analysis


Although clear links exist between social networking sites (SNS) and cyberbullying, limited studies have examined the content of SNS platforms. This study provides one of the few explorations of cyberbullying using Twitter data. Using supervised machine learning, it analyzes the disclosure of cyberbullying episodes on Twitter to understand who is posting and why they are posting about cyberbullying. Additionally, a qualitative content analysis of 500 tweets provides further insights into the characteristics of cyberbullying episodes. The findings reveal that aside from serving as a medium for cyberbullying, Twitter is also a space for bystanders to engage in ‘upstander’ behavior and where victims make connections and receive validation. We also found that cyberbullying targets extend beyond the peer group; random strangers, celebrities, and entire groups are victimized on SNS platforms via multiple forms of cyberbullying. The paper discusses how SNS platforms can become a part of the fight against cyberbullying.


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