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Dissecting the Predictors of Cyber-Aggression Through an Explainable Machine Learning Model.

Wenfeng Zhu1,2,3, Kai Wang1,2,3, Songyu Liu1,2,3

  • 1Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.

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Summary

This study identifies key factors influencing cyber-aggression in college students. Protective factors like anti-bullying attitudes can significantly reduce the impact of risk factors such as violence attitudes.

Keywords:
GAM theoryLightGBMSHAPcyber‐aggressionmachine learningtwo‐dimensional PD plots

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Area of Science:

  • Psychology
  • Criminology
  • Data Science

Background:

  • The General Aggression Model (GAM) posits cyber-aggression arises from individual and situational factors.
  • Previous research using linear models oversimplified the complex interplay of these factors.
  • A comprehensive analysis of multiple risk and protective factors is needed.

Purpose of the Study:

  • To identify and rank the importance of risk and protective factors in cyber-aggression.
  • To examine the interactions between these factors using advanced machine learning techniques.
  • To provide empirical support and expansion for the General Aggression Model (GAM).

Main Methods:

  • Utilized the Light Gradient Boosting Machine (LightGBM) for factor identification and ranking.
  • Employed SHAP (SHapley Additive exPlanations) to estimate individual variable predictive effects.
  • Conducted two-dimensional partial dependence (PD) plots to analyze predictor interactions.

Main Results:

  • The top five factors identified were: attitudes toward violence, revenge motivation, anti-bullying attitudes, moral disengagement, and anger rumination.
  • Significant interactions were found between protective factors (anti-bullying attitudes, moral reasoning) and risk factors (attitudes toward violence, revenge motivation, moral disengagement, anger rumination).
  • High levels of protective factors were shown to mitigate the influence of risk factors on cyber-aggression.

Conclusions:

  • Findings support and extend the General Aggression Model (GAM) in the context of cyber-aggression.
  • The study highlights the critical role of protective factors in mitigating cyber-aggression.
  • Results offer practical implications for developing interventions to reduce cyber-aggression among Chinese college students.