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“I know you are, but what am I?” Profiling cyberbullying based on charged language

Author

Listed:
  • Shuyuan Mary Ho

    (Florida State University)

  • Wenyi Li

    (Florida State University)

Abstract

Cyberbullying has become a global problem that victimizes social media users and threatens freedom of speech. Charged language against victims undermines the sharing of opinion in the absence of online oversight. Aggressive cyberbullies routinely patrol social media to identify victims, post abusive comments, and curtail public discourse. The victims typically suffer depression and may even attempt suicide. However, simply banning abusive words used by cyberbullies is not an effective response. This study examines the efficacy of using charged language-action cues as predictor variables to profile cyberbullying on Twitter. The study contributes to a proactive confirmation for computationally profiling cyberbullying based on charged language. Charged language-action cues can strongly profile cyberbullying activity with statistical significance and consistency. Big data profiling analytics based on charged language can prevent cyberbullies from possible criminal activity, protect potential victims, and provide a proactive measure to profile cyberbullying for mediation entities such as social media platforms, youth counselors and law enforcement agencies.

Suggested Citation

  • Shuyuan Mary Ho & Wenyi Li, 2022. "“I know you are, but what am I?” Profiling cyberbullying based on charged language," Computational and Mathematical Organization Theory, Springer, vol. 28(4), pages 293-320, December.
    • Handle: RePEc:spr:comaot:v:28:y:2022:i:4:d:10.1007_s10588-022-09360-5
    DOI: 10.1007/s10588-022-09360-5
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