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

  • S.I.: Social Cybersecurity in Times of Crisis
  • Published:
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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.

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Notes

  1. LSTM stands for long short-term memory.

  2. Cyberbullying “typically denotes repeated and hostile behavior performed by a group or an individual. Cyberaggression refers to “intentional harm delivered via electronic means to a person or a group of people who perceive such acts as offensive, derogatory, harmful, or unwanted” (Chatzakou et al. 2017).

  3. Keywords include nerd, gay, loser, freak, emo, whale, pig, fat, wannabe, poser, whore, should, die, slept, caught, suck, slut, live, afraid, fight, pussy, cunt, kill, dick, bitch (Cheng et al. 2019b; Nand et al. 2016).

  4. Using LIWC2015.

  5. LIWC2007.

  6. LIWC2015.

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Acknowledgements

The authors wish to thank Florida Center for Cybersecurity (FC2), also known as Cyber Florida, for the grant 3910-1007-00-B $50,000 07/01/18—06/30/20. The authors acknowledge the data collection and analysis efforts of Ming-Jung Chiu-Huang, Bismark Awuah Frimpong Ankamah, and Chung-Jui Lai. The first author wishes to thank Jeffrey M. Stanton for editorial comments and Conrad Metcalfe for assistance in editing.

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  1. College of Communication and Information, Florida State University, 250 Shores Building, 142 Collegiate Loop, Tallahassee, FL, 32306-2100, USA

    Shuyuan Mary Ho

  2. College of Education, Florida State University, Stone Building, 1114 W. Call Street, Tallahassee, FL, 32306-4450, USA

    Wenyi Li

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Ho, S.M., Li, W. “I know you are, but what am I?” Profiling cyberbullying based on charged language. Comput Math Organ Theory 28, 293–320 (2022). https://doi.org/10.1007/s10588-022-09360-5

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