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Research Article

Enabling New Strategies to Prevent Problematic Online Gambling: A Machine Learning Approach for Identifying At-risk Online Gamblers in FranceOpen Materials

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Pages 471-490 | Received 10 May 2022, Accepted 26 Dec 2022, Published online: 02 Feb 2023

References

  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Association. https://doi.org/10.1176/appi.books.9780890425596
  • Aslan, E. A., & Kilincel, O. (2021). The effect of social isolation on psychological stress and gambling in the COVID-19 pandemic. Annals of Clinical and Analytical Medicine, 12(01), 30–35. https://doi.org/10.4328/acam.20390
  • Auer, M., Malischnig, D., & Griffiths, M. (2014). Is “pop-up” messaging in online slot machine gambling effective as a responsible gambling strategy? Journal of Gambling Issues, 29(29), 1–10. https://doi.org/10.4309/jgi.2014.29.3
  • Autorité Nationale Des Jeux. (2021). Le marché des jeux en ligne au 3ème trimestre: stabilisation de la croissance à un niveau élevé. Retrieved April 20, 2022, from https://anj.fr/le-marche-des-jeux-en-ligne-au-3eme-trimestre-stabilisation-de-la-croissance-un-niveau-eleve
  • Benjamin, R. (2019). Race After Technology. Polity. Retrieved from https://www.ruhabenjamin.com/race-after-technology
  • Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7), 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2
  • Braverman, J., & Shaffer, H. J. (2012). How do gamblers start gambling: Identifying behavioural markers for high-risk internet gambling. European Journal of Public Health, 22(2), 273–278. https://doi.org/10.1093/eurpub/ckp232
  • Brownlow, L. (2021). A review of mHealth gambling apps in australia. Journal of Gambling Issues, 47(47), 1–19. https://doi.org/10.4309/jgi.2021.47.1
  • Burger, S. (2018). Introduction to machine learning with R: rigorous mathematical analysis. O’Reilly Media Inc. Retrieved from https://www.oreilly.com/library/view/introduction-to-machine/9781491976432/
  • Costes, J., & Eroukmanoff, V. (2017). Les pratiques de jeux d’argent sur Internet en France en 2017. Retrieved from https://www.ofdt.fr/odj/Note_ODJ_9.pdf
  • Costes, J., Eroukmanoff, V., & Tovar, M. (2014). Les joueurs de paris sportifs et hippiques en ligne. Les notes de l’Observatoire des jeux (Vol. 4). Retrieved from https://biblio.ludocorpus.org/sites/default/files/COSTES et al. 2014 Les joueurs de paris sportifs et hippiques en lign.pdfal. 2014 Les joueurs de paris sportifs et hippiques en lign.pdf
  • Costes, J., Richard, J. B., & Eroukmanoff, V. (2019). Les Problemes Lies Aux Jeux d’Argent en France, en 2019: Resultats du barometre de sante publique France. France. Retrieved from https://www.ofdt.fr/odj/NoteODJ12.pdf
  • Currie, S. R., Hodgins, D. C., & Casey, D. M. (2013). Validity of the Problem Gambling Severity Index Interpretive Categories. Journal of Gambling Studies, 29(2), 311–327. https://doi.org/10.1007/s10899-012-9300-6
  • D’agostino, R. B., Pencina, M. J., Massaro, J. M., & Coady, S. (2013). Cardiovascular disease risk assessment: Insights from Framingham. Global Heart, 8(1), 11. https://doi.org/10.1016/j.gheart.2013.01.001
  • Delfabbro, P., Osborn, A., Nevile, M., Skelt, L., & McMillan, J. (2007). Identifying Problem Gamblers in Gambling Venues. Retrieved from https://www.rccol.vic.gov.au/sites/default/files/2021-08/Exhibit%20RC0322%20Annexure%20z%2C%20The%20University%20of%20Adelaide%20Identifying%20Problem%20Gamblers%20in%20Gambling%20Venues%20Report%2C%20n.d.pdf
  • Eroukmanoff, V., Costes, J. -M., & Tovar, M. -L. (2014). Les joueurs de poker, une population présentant un profil particulier? Les notes de l’Observatoire des jeux (Vol. 3). Retrieved from https://www.economie.gouv.fr/files/files/directions_services/observatoire-des-jeux/Note_3.pdf
  • Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index: Final report. Canadian Centre on Substance Abuse. https://doi.org/10.1007/s10899-010-9224-y
  • Finkenwirth, S., MacDonald, K., Deng, X., Lesch, T., & Clark, L. (2020). Using machine learning to predict self-exclusion status in online gamblers on the PlayNow.Com platform in British Columbia. International Gambling Studies, 21(2), 220–237. https://doi.org/10.1080/14459795.2020.1832132
  • Gainsbury, S. M. (2015). Online Gambling Addiction: The Relationship Between Internet Gambling and Disordered Gambling. Current Addiction Reports, 2(2), 185–193. https://doi.org/10.1007/s40429-015-0057-8
  • Gebauer, L., LaBrie, R., & Shaffer, H. J. (2010). Optimizing DSM-IV-TR classification accuracy: A brief biosocial screen for detecting current gambling disorders among gamblers in the general household population. Canadian Journal of Psychiatry, 55(2), 82–90. https://doi.org/10.1177/070674371005500204
  • Griffiths, M., Wardle, H., Orford, J., Sproston, K., & Erens, B. (2009). Sociodemographic correlates of internet gambling: Findings from the 2007 British gambling prevalence survey. CyberPsychology & Behavior, 12(2), 199–202. https://doi.org/10.1089/cpb.2008.0196
  • Haeusler, J. (2016). Follow the money: Using payment behaviour as predictor for future self-exclusion. International Gambling Studies, 16(2), 246–262. https://doi.org/10.1080/14459795.2016.1158306
  • Hancock, L., Schellinck, T., & Schrans, T. (2008). Gambling and corporate social responsibility (CSR): Re-defining industry and state roles on duty of care, host responsibility and risk management. Policy and Society, 27(1), 55–68. https://doi.org/10.1016/j.polsoc.2008.07.005
  • Hastie, T., Qian, J., & Tay, K. (2021). An Introduction to glmnet. Retrieved from https://cran.r-project.org/web/packages/glmnet/vignettes/glmnet.pdf
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. Retrieved from https://web.stanford.edu/ hastie/ElemStatLearn/
  • Hayer, T., & Meyer, G. (2011). Internet Self-Exclusion: Characteristics of Self-Excluded Gamblers and Preliminary Evidence for Its Effectiveness. International Journal of Mental Health and Addiction, 9(3), 296–307. https://doi.org/10.1007/s11469-010-9288-z
  • Heirene, R. M., & Gainsbury, S. M. (2021). Encouraging and evaluating limit-setting among on-line gamblers: A naturalistic randomized controlled trial. Addiction, [Online], 116(10), 1–10. https://doi.org/10.1111/add.15471
  • Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise. Little Brown Spark.
  • Kim, H. S., Wohl, M. J. A., Stewart, M. J., Sztainert, T., & Gainsbury, S. M. (2014). Limit your time, gamble responsibly: Setting a time limit (via pop-up message) on an electronic gaming machine reduces time on device. International Gambling Studies, 14(2), 266–278. https://doi.org/10.1080/14459795.2014.910244
  • Kingma, S. F. (2015). Paradoxes of risk management: Social responsibility and self-exclusion in Dutch casinos. Culture and Organization, 21(1), 1–22. https://doi.org/10.1080/14759551.2013.795152
  • Korn, D. A., & Shaffer, H. J. (1999). Gambling and the Health of the Public: Adopting a Public Health Perspective. Journal of Gambling Studies, 15(4), 289–365. https://doi.org/10.1023/a:1023005115932
  • Krebs, P., & Duncan, D. T. (2015). Health app use among US mobile phone owners: A national survey. JMIR MHealth and UHealth, 3(4), e101. https://doi.org/10.2196/mhealth.4924
  • LaPlante, D. A., Nelson, S. E., & Gray, H. M. (2014). Breadth and depth involvement: Understanding Internet gambling involvement and its relationship to gambling problems. Psychology of Addictive Behaviors, 28(2), 396–403. https://doi.org/10.1037/a0033810
  • Lecomte, T., Potvin, S., Corbière, M., Guay, S., Samson, C., Cloutier, B. Francoeur, A., Pennou, A., & Khazaal, Y. (2020). Mobile apps for mental health issues: Meta-review of meta-analyses. JMIR MHealth and UHealth, 8(5), 1–14. https://doi.org/10.2196/17458
  • Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of Pathological gamblers. The American Journal of Psychiatry, 144(9), 1184–1188. https://doi.org/10.1176/ajp.144.9.1184
  • Lugo, A., Stival, C., Paroni, L., Amerio, A., Carreras, G., Gorini, G. Mastrobattista, L., Minutillo, A., Mortali, C., Odone, A., Pacifici, R., Tinghino, B., & Gallus, S. (2021). The impact of COVID-19 lockdown on gambling habit : A cross-sectional study from Italy. Journal of Behavioral Addictions, 10(3), 1–11. https://doi.org/10.1556/2006.2021.00033
  • Lupton, D. (2015). Digital Sociology. Gastronomía ecuatoriana y turismo local (Vol. 1). Routledge.
  • Luquiens, A., Tanguy, M. -L., Benyamina, A., Lagadec, M., Aubin, H. J., & Reynaud, M. (2016). Tracking online poker problem gamblers with player account-based gambling data only. International Journal of Methods in Psychiatric Research, 25(4), 332–342. https://doi.org/10.1002/mpr.1510
  • Luquiens, A., Vendryes, D., Aubin, H. J., Benyamina, A., Gaiffas, S., & Bacry, E. (2018). Description and assessment of trustability of motives for self-exclusion reported by online poker gamblers in a cohort using account-based gambling data. BMJ Open, 8(12), 1–8. https://doi.org/10.1136/bmjopen-2018-022541
  • Markham, F., Young, M., & Doran, B. (2013). Detection of Problem Gambler Subgroups Using Recursive Partitioning. International Journal of Mental Health and Addiction, 11(3), 281–291. https://doi.org/10.1007/s11469-012-9408-z
  • McCormick, A. V., Cohen, I. M., & Davies, G. (2018). Differential effects of formal and informal gambling on symptoms of problem gambling during voluntary self-exclusion. Journal of Gambling Studies, 34(3), 1013–1031. https://doi.org/10.1007/s10899-018-9743-5
  • Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C. -C., & Lin, C. -C. (2021). Misc Functions of the Department of Statistics, Probability Theory Group (e1071). Retrieved from https://cran.r-project.org/web//packages/e1071/index.html
  • Monaghan, S., & Blaszczynski, A. (2010). Impact of mode of display and message content of responsible gambling signs for electronic gaming machines on regular gamblers. Journal of Gambling Studies, 26(1), 67–88. https://doi.org/10.1007/s10899-009-9150-z
  • Mora-Salgueiro, J., García-Estela, A., Hogg, B., Angarita-Osorio, N., Amann, B. L., Carlbring, P. Jimenez-Murcia, S., Perez-Sola, V., & Colom, F. (2021). The Prevalence and Clinical and Sociodemographic Factors of Problem Online Gambling: A Systematic Review. Journal of Gambling Studies, 37(3), 899–926. https://doi.org/10.1007/s10899-021-09999-w
  • Nelson, S. E., LaPlante, D. A., Peller, A. J., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008). Real limits in the virtual world: Self-limiting behavior of internet gamblers. Journal of Gambling Studies, 24(4), 463–477. https://doi.org/10.1007/s10899-008-9106-8
  • Olason, D. T., Kristjansdottir, E., Einarsdottir, H., Haraldsson, H., Bjarnason, G., & Derevensky, J. L. (2011). Internet Gambling and Problem Gambling Among 13 to 18 Year Old Adolescents in Iceland. International Journal of Mental Health and Addiction, 9(3), 257–263. https://doi.org/10.1007/s11469-010-9280-7
  • Palmer du Preez, K., Landon, J., Bellringer, M., Garrett, N., & Abbott, M. (2016). The Effects of Pop-up Harm Minimisation Messages on Electronic Gaming Machine Gambling Behaviour in New Zealand. Journal of Gambling Issues, 32(4), 1115–1126. https://doi.org/10.1007/s10899-016-9603-0
  • Percy, C., França, M., Dragičević, S., & d’Avila Garcez, A. (2016). Predicting online gambling self-exclusion: An analysis of the performance of supervised machine learning models. International Gambling Studies, 16(2), 193–210. https://doi.org/10.1080/14459795.2016.1151913
  • Peter, S. C., Brett, E. I., Suda, M. T., Leavens, E. L. S., Miller, M. B., Leffingwell, T. R., Whelan, J. P., & Meyers, A. W. (2019). A Meta-analysis of Brief Personalized Feedback Interventions for Problematic Gambling. Journal of Gambling Studies, 35(2), 447–464. https://doi.org/10.1007/s10899-018-09818-9
  • Petry, N. M. (2006). Internet gambling: An emerging concern in family practice medicine? Family Practice, 23(4), 421–426. https://doi.org/10.1093/fampra/cml005
  • Petry, N. M., & Weinstock, J. (2007). Internet gambling is common in college students and associated with poor mental health. American Journal on Addictions, 16(5), 325–330. https://doi.org/10.1080/10550490701525673
  • Philander, K. S. (2014). Identifying high-risk online gamblers: A comparison of data mining procedures. International Gambling Studies, 14(1), 53–63. https://doi.org/10.1080/14459795.2013.841721
  • Philander, K. S., & MacKay, T. L. (2014). Online gambling participation and problem gambling severity: Is there a causal relationship? International Gambling Studies, 14(2), 214–227. https://doi.org/10.1080/14459795.2014.893585
  • Potenza, M. N., Balodis, I. M., Derevensky, J., Grant, J. E., Petry, N. M., Verdejo-Garcia, A., & Yip, S. W. (2019). Gambling Disorder. Nature Reviews Disease Primers, 5(1), 1–21. https://doi.org/10.1038/s41572-019-0099-7
  • Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring Fairness in Machine Learning to Advance Health Equity. Annals of Internal Medicine, 169(12), 886–872. https://doi.org/10.7326/M18-1990
  • R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from http://www.r-project.org/
  • Reynolds, J., Kairouz, S., Ilacqua, S., & French, M. (2020). Responsible Gambling: A Scoping Review. Critical Gambling Studies, 1(1), 23–39. https://doi.org/10.29173/cgs42
  • Samuelsson, E., Wennberg, P., & Sundqvist, K. (2019). Gamblers’ (mis-)interpretations of Problem Gambling Severity Index items: Ambiguities in qualitative accounts from the Swedish Longitudinal Gambling Study. NAD Nordic Studies on Alcohol and Drugs, 36(2), 140–160. https://doi.org/10.1177/1455072519829407
  • Sarkar, S., Weyde, T., Garcez, A., Slabaugh, G. G., Dragicevic, S., & Percy, C. (2016). Accuracy and interpretability trade-offs in machine learning applied to safer gambling. CEUR Workshop Proceedings. Retrieved from https://openaccess.city.ac.uk/id/eprint/16484/
  • Sharman, S., Dreyer, J., Aitken, M., Clark, L., & Bowden Jones, H. (2015). Rates of problematic gambling in a British homeless sample: A preliminary study. Journal of Gambling Studies, 31(2), 525–532. https://doi.org/10.1007/s10899-014-9444-7
  • Shaw, C. A., Hodgins, D. C., Williams, R. J., Belanger, Y. D., Christensen, D. R., El-Guebaly, N. McGrath, D. S., Nicoll, F., Smith, G. J., & Stevens, R. M. G. (2021). Gambling in Canada During the COVID Lockdown: Prospective National Survey. Journal of Gambling Studies, 38(2), 371–396. https://doi.org/10.1007/s10899-021-10073-8
  • Stark, S., & Robinson, J. (2021). Online gambling in unprecedented times: Risks and safer gambling strategies during the COVID-19 pandemic. Journal of Gambling Issues, 47(17), 409–423. https://doi.org/10.4309/jgi.2021.47.17
  • Stewart, M. J., & Wohl, M. J. A. (2012). Pop-up messages, dissociation, and craving: How monetary limit reminders facilitate adherence in a session of slot machine gambling. Psychology of Addictive Behaviors, 27(1), 268–273. https://doi.org/10.1037/a0029882
  • Stinchfield, R. (2003). Reliability, Validity, and Classification Accuracy of a Measure of DSM-IV Diagnostic Criteria for Pathological Gambling. The American Journal of Psychiatry, 160(1), 180–182. https://doi.org/10.1176/appi.ajp.160.1.180
  • Stone, C. A., Romild, U., Abbott, M., Yeung, K., Billi, R., & Volberg, R. (2015). Effects of different screening and scoring thresholds on PGSI gambling risk segments. International Journal of Mental Health and Addiction, 13(1), 82–102. https://doi.org/10.1007/s11469-014-9515-0
  • Therneau, T., Atkinson, B., & Ripley, B. (2019). Recursive Partitioning and Regression Trees (rpart). Retrieved from https://cran.r-project.org/web/packages/rpart/rpart.pdf
  • Valleur, M. (2015). Gambling and gambling-related problems in France. Addiction, 110(12), 1872–1876. https://doi.org/10.1111/add.12967
  • Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (4th ed.). Springer. Retrieved from https://www.stats.ox.ac.uk/pub/MASS4/
  • Williams, R. J., & Volberg, R. A. (2014). The classification accuracy of four problem gambling assessment instruments in population research. International Gambling Studies, 14(1), 15–28. https://doi.org/10.1080/14459795.2013.839731
  • Wynne, H. (2003). Introducing the Canadian problem gambling index. Edmonton. Retrieved from http://classes.uleth.ca/201201/hlsc3700a/The Canadian Problem Gambling Index.pdf
  • Xuan, Z., & Shaffer, H. (2009). How do gamblers end gambling: Longitudinal analysis of internet gambling behaviors prior to account closure due to gambling related problems. Journal of Gambling Studies, 25(2), 239–252. https://doi.org/10.1007/s10899-009-9118-z
  • Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32–35. https://doi.org/10.1002/1097-014219503:1<32:AID-CNCR2820030106>3.0.CO;2-3

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