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Guided by ecological systems theory, this study applies explainable machine learning (ML) to examine factors associated with cyberbullying perpetration across individual, family, peer, class, school, and online contexts. Questionnaire data from 2286 adolescents (\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>age<\/jats:sub>\n                      = 13.46 years, SD = 0.93; 11\u201316 years) were analyzed. Random Forest and XGBoost achieved out\u2010of\u2010sample accuracies of 87.35% and 85.95%, respectively. Model\u2010based importance analyses consistently highlighted\n                      <jats:italic>Childhood Psychological Abuse<\/jats:italic>\n                      ,\n                      <jats:italic>Adverse Peer Interactions<\/jats:italic>\n                      , and\n                      <jats:italic>Cyberbullying Victimization<\/jats:italic>\n                      as the highest\u2010ranked predictors. At the system level, variables from the\n                      <jats:italic>Family<\/jats:italic>\n                      ,\n                      <jats:italic>Individual<\/jats:italic>\n                      and\n                      <jats:italic>Cyber<\/jats:italic>\n                      contexts accounted for a substantial share of model importance, indicating their salience for intervention design. These findings prioritize psychosocial targets for prevention and demonstrate how explainable ML can synthesize questionnaire data to inform multi\u2010tiered strategies against adolescent cyberbullying.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>By adopting an ecosystem\u2010based perspective, this study utilizes comprehensive, multi\u2010level datasets to model adolescent cyberbullying.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Explainable machine learning techniques are employed to systematically identify and interpret key predictors of cyberbullying perpetration.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Childhood psychological abuse exhibits strong predictive power for adolescent cyberbullying perpetration.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The study proposes a methodological framework for applying explainable machine learning to structured questionnaire data in psychological research.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/desc.70230","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T06:48:00Z","timestamp":1780382880000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unveiling Roots of Chinese Adolescent Cyberbullying Through Explainable Machine Learning Approach"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6355-2224","authenticated-orcid":false,"given":"Wanghao","family":"Dong","sequence":"first","affiliation":[{"name":"Key Laboratory of Adolescent Cyberpsychology and Behavior Ministry of Education Wuhan Hubei China"},{"name":"School of Psychology Central China Normal University  Wuhan Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0607-1507","authenticated-orcid":false,"given":"Yinghui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Management Wuhan University of Technology Wuhan Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Manchester Institute of Education The University of Manchester Manchester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4472-189X","authenticated-orcid":false,"given":"Xingchao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Educational Science Shanxi University Taiyuan Shanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijun","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Adolescent Cyberpsychology and Behavior Ministry of Education Wuhan Hubei China"},{"name":"Faculty of Public Administration Beijing City University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"e_1_2_13_2_1","unstructured":"*Denotes Index of Literature on Measurement Tools."},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1745\u20109125.1992.tb01093.x"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nut.2024.112674"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11205-025-03614-w"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2020.106315"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.avb.2015.05.014"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1037\/0022\u20103514.71.2.364"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2017.02.060"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/ab.21670"},{"key":"e_1_2_13_11_1","volume-title":"Predicting Cyberbullying: Research, Theory, and Intervention","author":"Barlett C. 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