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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. [ABSTRACT FROM AUTHOR]

This article examines Canada's first internet gambling website blocking scheme, which was enacted in Quebec as part of the implementation of the province's 2015 budget. Using qualitative research methods, the article illustrates the complexities of regulating online gambling. Influenced by critical sociological and anthropological studies of gambling, and taking a socio-legal, governmentality perspective, it shows how socio-legal studies can illuminate research on the regulation of gambling, and how the study of online gambling can, as a sentinel site for the regulation of online consumption, contribute to the development of socio-legal studies. Our analysis shows that the governmentality of online gambling is framed so as to exclude 1) a range of risks (e.g., related to consumer profiling and the capacity to stimulate "addictive consumption"), 2) the heterogeneity of everyday experience that connects online gambling with online addictive consumption more generally, and 3) a range of possibilities for governing online gambling otherwise. [ABSTRACT FROM AUTHOR]