Using artificial intelligence algorithms to predict self-reported problem gambling among online casino gamblers from different countries using account-based player data

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Hopfgartner, N, Auer, M, Helic, D and Griffiths, MD ORCID: https://orcid.org/0000-0001-8880-6524, 2024. Using artificial intelligence algorithms to predict self-reported problem gambling among online casino gamblers from different countries using account-based player data. International Journal of Mental Health and Addiction. ISSN 1557-1874

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Official URL: https://doi.org/10.1007/s11469-024-01312-1

Abstract

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.

Item Type:	Journal article
Publication Title:	International Journal of Mental Health and Addiction
Creators:	Hopfgartner, N., Auer, M., Helic, D. and Griffiths, M.D.
Publisher:	Springer Science and Business Media LLC
Date:	7 May 2024
ISSN:	1557-1874
Identifiers:	Number Type 10.1007/s11469-024-01312-1 DOI 1892961 Other
Rights:	© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Social Sciences
Record created by:	Laura Ward
Date Added:	09 May 2024 08:58
Last Modified:	09 May 2024 08:58
URI:	https://irep.ntu.ac.uk/id/eprint/51416