ABSTRACT
Gambling activities are rapidly migrating online. Algorithms that effectively detect at-risk users could improve the prevention of online gambling-related harms. We sought to identify machine learning algorithms capable of detecting self-reported gambling problems using demographic and behavioral data. Online gamblers were recruited from all licensed online gambling platforms in France by the French Online Gambling Regulatory Authority (ARJEL). Participants completed the Problem Gambling Severity Index (PGSI), and these data were merged and synchronized with past-year online gambling behaviors recorded on the operators’ websites. Among all participants (N = 9,306), some users reported betting exclusively on sports (N = 1,183), horseracing (N = 1,711), or poker (N = 2,442) activities. In terms of Area Under the Receiver Operating Characteristic Curve (AUC), our algorithms showed excellent performance in classifying individuals at a moderate-to-high (PGSI 5+; AUC = 83.20%), or high (PGSI 8+; AUC = 87.70%) risk for experiencing gambling-related harms. Further, these models identified novel behavioral markers of harmful online gambling for future research. We conclude that machine learning can be used to detect online gamblers at-risk for experiencing gambling problems. Using algorithms like these, operators and regulators can develop targeted harm prevention and referral-to-treatment initiatives for at-risk users.
Acknowledgements
We wish to thank ARJEL for their support and data access. Thank you to Benjamin Behaghel, Philippe Brandt, Gaëlle Chalet, Charles Coppolani, Benjamin Donnot, Marie Grall-Bronnec, Antoine Guillot, Carole Leduc, Amandine Luquiens, Clément Martin-Saint-Léon, Jérome Rabenou, and Marie-Ange Santarelli for their contributions to the project
Disclosure statement
SK, JMC, PDD, CC, and VE declare no competing interests. WSM previously received training and funding from The Centre for Gambling Research at UBC, a research laboratory jointly supported by the Government of British Columbia and the British Columbia Lottery Corporation (BCLC; a Canadian Crown Corporation).
Data availability
The data in this study index transactions made on various licensed and private gambling websites. The agreement governing our use of these data stipulates that they may not be publicly released.
Open Scholarship
This article has earned the Center for Open Science badge for Open Materials. The materials are openly accessible at https://doi.org/10.17605/OSF.IO/K2CDM
Ethical approval
National Commission on Information Technology and Civil Liberties (2015-188).
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/14459795.2022.2164042
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
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Notes on contributors
Sylvia Kairouz
Sylvia Kairouz is a professor in the Department of Sociology and Anthropology at Concordia University. Her work pertains to gambling and new forms of mobile gaming. She holds an FQRSC research chair on the study of gambling and is the head of the Lifestyle and Addiction Research Lab at Concordia University.
Jean-Michel Costes
Jean-Michel Costes Sociologist, demographer and epidemiologist, he is associated researcher at the Concordia University Research Chair on Gambling Studies. Former director of the French monitoring centre on drugs and drug addictions and Secretary-General of the French monitoring centre on gambling, he is member of the college of the French National Gambling Authority.
W. Spencer Murch
W. Spencer Murch is a cognitive psychologist and Horizon Postdoctoral Fellow at the Concordia University Research Chair on Gambling Studies.
Pascal Doray-Demers
Pascal Doray-Demers is a political scientist and Horizon Postdoctoral Fellow at the Concordia University Research Chair on Gambling Studies.
Clément Carrier
Clément Carrier is a data scientist and researcher with ENSAE Junior Études.
Vincent Eroukmanoff
Vincent Eroukmanoff is a statistician specializing in drugs and addictive behaviours at Observatoire des Jeux.