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This study used the light gradient boosting machine (LightGBM) to identify and rank the importance of various risk and protective factors in cyber\u2010aggression. The SHAP (SHapley Additive exPlanations) technique estimated each variable's predictive effects, and two\u2010dimensional partial dependence (PD) Plots examined interactions among predictors. Among 30 potential factors, the top five were attitudes toward violence, revenge motivation, anti\u2010bullying attitudes, moral disengagement, and anger rumination. PD analysis showed significant interactions between protective factors (anti\u2010bullying attitudes and moral reasoning) and risk factors (attitudes toward violence, revenge motivation, moral disengagement, and anger rumination). High scores on protective factors mitigated the impact of risk factors on cyber\u2010aggression. These findings support and expand GAM, offering implications for reducing cyber\u2010aggression among Chinese college students.<\/jats:p>","DOI":"10.1002\/ab.70013","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T06:14:11Z","timestamp":1734502451000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Dissecting the Predictors of Cyber\u2010Aggression Through an Explainable Machine Learning Model"],"prefix":"10.1002","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8585-8337","authenticated-orcid":false,"given":"Wenfeng","family":"Zhu","sequence":"first","affiliation":[{"name":"Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior Tianjin Normal University Tianjin China"},{"name":"Faculty of Psychology Tianjin Normal University Tianjin China"},{"name":"Tianjin Key Laboratory of Student Mental Health 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