Using Artificial Intelligence Algorithms to Predict Self-Reported Problem Gambling Among Online Casino Gamblers from Different Countries Using Account-Based Player Data.
The prevalence of online gambling and the potential for related harm necessitate predictive models for early detection of problem gambling. The present study expands upon prior research by incorporating a cross-country approach to predict self-reported problem gambling using player-tracking data in...
| Publicado en: | International Journal of Mental Health & Addiction Vol. 24; no. 2; pp. 981 - 1004 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Apr2026
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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=193492993&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193492993 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 15571874 46AW jtl: International Journal of Mental Health & Addiction issn: 15571874 maglogo: N pubinfo: dt: Apr2026 vid: 24 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 193492993 10.1007/s11469-024-01312-1 ppf: 981 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: Using Artificial Intelligence Algorithms to Predict Self-Reported Problem Gambling Among Online Casino Gamblers from Different Countries Using Account-Based Player Data. aug: au: Hopfgartner, Niklas Auer, Michael Helic, Denis Griffiths, Mark D. affil: Neccton GmbH, Davidgasse 5, 7052, Müllendorf, Austria https://ror.org/00d7xrm67 Institute of Interactive Systems and Data Science, Graz University of Technology, Sandgasse 36/III, 8010, Graz, Austria https://ror.org/04xyxjd90 International Gaming Research Unit, Psychology Department, Nottingham Trent University, 50 Shakespeare Street, NG1 4FQ, Nottingham, UK su: Compulsive gambling Compulsive gamblers Artificial intelligence Cross-cultural studies Machine learning Prediction models Internet gambling sug: subj: Compulsive gambling Compulsive gamblers Artificial intelligence Cross-cultural studies Other Gambling Industries All other gambling industries Machine learning Prediction models Internet gambling keyword: PGSI Problem gambling Problem Gambling Severity Index Psychology and Cognitive Sciences Psychology Responsible gambling Responsible gambling tools PGSI Problem gambling Problem Gambling Severity Index Psychology and Cognitive Sciences Psychology Responsible gambling Responsible gambling tools ab: The prevalence of online gambling and the potential for related harm necessitate predictive models for early detection of problem gambling. The present study expands upon prior research by incorporating a cross-country approach to predict self-reported problem gambling using player-tracking data in an online casino setting. Utilizing a secondary dataset comprising 1743 British, Canadian, and Spanish online casino gamblers (39% female; mean age = 42.4 years; 27.4% scoring 8 + on the Problem Gambling Severity Index), the present study examined the association between demographic, behavioral, and monetary intensity variables with self-reported problem gambling, employing a hierarchical logistic regression model. The study also tested the efficacy of five different machine learning models to predict self-reported problem gambling among online casino gamblers from different countries. The findings indicated that behavioral variables, such as taking self-exclusions, frequent in-session monetary depositing, and account depletion, were paramount in predicting self-reported problem gambling over monetary intensity variables. The study also demonstrated that while machine learning models can effectively predict problem gambling across different countries without country-specific training data, incorporating such data improved the overall model performance. This suggests that specific behavioral patterns are universal, yet nuanced differences across countries exist that can improve prediction models. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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