Exploring Predictors of Bullying Perpetration Among Adolescents Using Machine Learning Approach.
This study used machine learning methods to detect risk and protective factors for bullying perpetration in adolescents. The study sample consisted of 777 students with an age range of 11 to 16 years old. Multidimensional data covering both individual and environmental levels were collected. Individ...
| Publicado en: | Journal of Interpersonal Violence Vol. 41; no. 11/12; pp. 3077 - 3102 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Jun2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193488350&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193488350 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08862605 JIV jtl: Journal of Interpersonal Violence issn: 08862605 maglogo: Y pubinfo: dt: Jun2026 vid: 41 iid: 11/12 pid: 344 pub: Sage Publications Inc. artinfo: ui: 193488350 10.1177/08862605251336348 ppf: 3077 ppct: 25 formats: tig: atl: Exploring Predictors of Bullying Perpetration Among Adolescents Using Machine Learning Approach. aug: au: Zhou, Huiling Zheng, Qubo Jiang, Huaibin Lu, Jiamei affil: Department of Psychology, Shanghai Normal University, China School of Computer and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang, China School of Education, Fujian Polytechnic Normal University, Fuzhou, China su: China Bullying prevention Psychology of middle school students Parent-child relationships Affinity groups Self-control Ethics Bullying Psychological disengagement Adolescence Random forest algorithms Risk assessment Prediction models Receiver operating characteristic curves Research funding Logistic regression analysis Questionnaires Research evaluation Surveys Machine learning Data analysis software Accuracy Algorithms sug: subj: Bullying prevention Psychology of middle school students Parent-child relationships Affinity groups Self-control Ethics Bullying Psychological disengagement Adolescence China Random forest algorithms Risk assessment Prediction models Receiver operating characteristic curves Research funding Logistic regression analysis Questionnaires Research evaluation Surveys Machine learning Data analysis software Accuracy Algorithms keyword: adolescent bullying perpetration machine learning moral disengagement risk and protective factors self-control SHAP adolescent bullying perpetration machine learning moral disengagement risk and protective factors self-control SHAP ab: This study used machine learning methods to detect risk and protective factors for bullying perpetration in adolescents. The study sample consisted of 777 students with an age range of 11 to 16 years old. Multidimensional data covering both individual and environmental levels were collected. Individual factors included moral disengagement, normative beliefs about aggression, neuroticism, and self-control; environmental factors included parent–child relationships, deviant peer affiliation, school connection, and violent media exposure. The current study tested and compared six machine learning algorithms: Logistic Regression, Random Forest, Gradient Boosting Decision Tree, XGBoost, LightGBM, and Stacking, to detect risk and protective factors for bullying behavior. The results demonstrated that: (a) the Random Forest algorithm performed optimally, with recall, F1 score, and area under the curve values of 0.9394, 0.8516, and 0.8043, respectively; (b) both Gini importance and SHapley Additive exPlanations (SHAP) values identified self-control as the most significant protective factor, while moral disengagement was identified as the most influential risk factor. The recommended model not only provides an application value in preventing bullying but also provides a scientific basis for developing targeted interventions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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