{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:37:57Z","timestamp":1777855077599,"version":"3.51.4"},"reference-count":50,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Interpers Violence"],"abstract":"<jats:p>\n                    This study aimed to identify key risk factors and predict digital intimate partner violence (DIPV) exposure and perpetration among university students using machine learning (ML) algorithms. A cross-sectional online survey was conducted with 1,764 university students (age range\u2009=\u200918\u201341\u2009years,\n                    <jats:italic toggle=\"yes\">M<\/jats:italic>\n                    \u2009=\u200920.8; 87.2% female, 12.8% male) selected through snowball sampling from a large public university in T\u00fcrkiye. The survey included sociodemographic, lifestyle, and relationship variables, along with the Digital Intimate Partner Violence Scale. Six ML models were used: Logistic Regression (LR), XGBoost, Gradient Boosting (GB), Random Forest (RF), LightGBM, and Support Vector Machines (SVM). Model performance was evaluated using accuracy, precision, recall, F1 score, and receiver operating characteristics-area under the curve (ROC-AUC). XGBoost achieved the highest performance (AUC\u2009=\u20090.996), followed closely by RF and LightGBM (AUC\u2009=\u20090.995). LR and GB also performed well (AUC\u2009=\u20090.992), while SVM had slightly lower performance (AUC\u2009=\u20090.989). SHapley Additive exPlanations analysis revealed that domestic violence history, urban residence, father\u2019s low education, short relationship duration, and frequent digital communication were risk factors. High income perception and non-smoking reduced DIPV risk. ML models, particularly XGBoost, effectively predict DIPV. Socioeconomic and psychosocial factors should be targeted in prevention efforts, alongside digital literacy and support services.\n                  <\/jats:p>","DOI":"10.1177\/08862605261436879","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T10:45:15Z","timestamp":1777200315000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["The Dark Side of Love: Prediction of Digital Intimate Partner Violence and Associated Factors Among University Students Using Machine Learning"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4352-1124","authenticated-orcid":false,"given":"G\u00f6zde","family":"\u00d6zsezer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6454-714X","authenticated-orcid":false,"given":"Hilalnur","family":"K\u00fc\u00e7\u00fckakg\u00fcn","sequence":"additional","affiliation":[{"name":"\u00c7anakkale Onsekiz Mart University, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helin","family":"Yilmaz","sequence":"additional","affiliation":[{"name":"\u00c7anakkale Onsekiz Mart University, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azra","family":"Ozan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,4,26]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/0886260517719541"},{"key":"e_1_3_3_3_1","first-page":"7","article-title":"Young adults\u2019 motivations for digital dating violence behaviors","volume":"22","author":"Bacio\u011flu S.","year":"2024","unstructured":"Bacio\u011flu S., Ak\u00e7ay F. 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