Using Artificial Intelligence Algorithms to Predict Self-Reported Problem Gambling Among Online Casino Gamblers from Different Countries Using Account-Based Player Data

Abbott, M. W., Romild, U., & Volberg, R. A. (2014). Gambling and problem gambling in Sweden: Changes between 1998 and 2009. Journal of Gambling Studies, 30(4), 985–999.

Article  PubMed  Google Scholar 

Andrade, M., Sharman, S., Xiao, L. Y., & Newall, P. W. (2023). Safer gambling and consumer protection failings among 40 frequently visited cryptocurrency-based online gambling operators. Psychology of Addictive Behaviors, 37(3), 545–557.

Article  PubMed  Google Scholar 

Andrews, J. L., Ahmed, S. P., & Blakemore, S. J. (2021). Navigating the social environment in adolescence: The role of social brain development. Biological Psychiatry, 89(2), 109–118.

Article  PubMed  Google Scholar 

Auer, M., Griffiths, M.D. (2023b). Predicting high-risk gambling based on the first seven days of gambling activity after registration using account-based tracking data. International Journal of Mental Health and Addiction. Advance online publication. https://doi.org/10.1007/s11469-023-01056-4

Auer, M., Hopfgartner, N., Helic, D., & Griffiths, M. D. (2023). Self-reported deposits versus actual deposits in online gambling: An empirical study. Journal of Gambling Studies. Advance online publication. https://doi.org/10.1007/s10899-023-10230-1

Auer, M., & Griffiths, M. D. (2023a). Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting. Journal of Gambling Studies, 39(3), 1273–1294.

Article  PubMed  Google Scholar 

Auer, M., & Griffiths, M. D. (2023c). An empirical attempt to operationalize chasing losses in gambling utilizing account-based player tracking data. Journal of Gambling Studies, 39(4), 1547–1561.

Article  PubMed  Google Scholar 

Auer, M., & Griffiths, M. D. (2023d). The relationship between structural characteristics and gambling behaviour: An online gambling player tracking study. Journal of Gambling Studies, 39(1), 265–279.

Article  PubMed  Google Scholar 

Auer, M., & Griffiths, M. D. (2023e). Attitude towards deposit limits and relationship with their account-based data among a sample of German online slots players. Journal of Gambling Studies, 39(3), 1319–1336.

Article  PubMed  Google Scholar 

Barrault, S., & Varescon, I. (2013). Cognitive distortions, anxiety, and depression among regular and pathological gambling online poker players. Cyberpsychology, Behavior and Social Networking, 16(3), 183–188.

Article  PubMed  Google Scholar 

Blaszczynski, A., & Nower, L. (2010). Instrumental tool or drug: Relationship between attitudes to money and problem gambling. Addiction Research & Theory, 18(6), 681–691.

Article  Google Scholar 

Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376. https://doi.org/10.1038/nrn3475

Article  CAS  PubMed  Google Scholar 

Catania, M., & Griffiths, M. D. (2021). Understanding online voluntary self-exclusion in gambling: An empirical study using account-based behavioral tracking data. International Journal of Environmental Research and Public Health, 18(4), 2000.

Article  PubMed  PubMed Central  Google Scholar 

Catania, M., & Griffiths, M. D. (2023). Analyzing consumer protection for gamblers across different online gambling operators: A replication study. International Journal of Mental Health and Addiction, 21(3), 1882–1897.

Article  Google Scholar 

Chóliz, M. (2016). The challenge of online gambling: The effect of legalization on the increase in online gambling addiction. Journal of Gambling Studies, 32(2), 749–756.

Article  PubMed  Google Scholar 

Chóliz, M., Marcos, M., & Lázaro-Mateo, J. (2021). The risk of online gambling: A study of gambling disorder prevalence rates in Spain. International Journal of Mental Health and Addiction, 19, 404–417.

Article  Google Scholar 

Dowling, N. A., Merkouris, S. S., Manning, V., Volberg, R., Lee, S. J., Rodda, S. N., & Lubman, D. I. (2018). Screening for problem gambling within mental health services: A comparison of the classification accuracy of brief instruments. Addiction, 113(6), 1088–1104.

Article  PubMed  Google Scholar 

Ekholm, O., Eiberg, S., Davidsen, M., Holst, M., Larsen, C. V., & Juel, K. (2014). The prevalence of problem gambling in Denmark in 2005 and 2010: A sociodemographic and socioeconomic characterization. Journal of Gambling Studies, 30, 1–10.

Article  PubMed  Google Scholar 

Elton-Marshall, T., Leatherdale, S. T., & Turner, N. E. (2016). An examination of internet and land-based gambling among adolescents in three Canadian provinces: Results from the youth gambling survey (YGS). BMC Public Health, 16(1), 277.

Article  PubMed  PubMed Central  Google Scholar 

Ferris, J., & Wynne, H. J. (2001). The Canadian Problem Gambling Index: Final report. Canadian Centre on Substance Abuse.

Finkenwirth, S., MacDonald, K., Deng, X., Lesch, T., & Clark, L. (2021). 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.

Article  Google Scholar 

Gambling Commission (2023). Statistics on participation and problem gambling. https://www.gamblingcommission.gov.uk/statistics-and-research/publication/gambling-participation-and-the-prevalence-of-problem-gambling-survey-final

Gambling Commission (2022a). £1.17m fine for marketing to vulnerable consumers. https://www.gamblingcommission.gov.uk/news/article/gbp1-17m-fine-for-marketing-to-vulnerable-consumers

Gambling Commission (2022b). £9.4m fine for online operator 888. https://www.gamblingcommission.gov.uk/news/article/gbp9-4m-fine-for-online-operator-888

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.

Article  PubMed  Google Scholar 

Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.

Book  Google Scholar 

Griffiths, M. D., & Auer, M. (2016). Should voluntary “self-exclusion” by gamblers be used as a proxy measure for problem gambling? Addiction Medicine & Therapy, 2(2), 31–33.

Google Scholar 

Griffiths, M. D., & Parke, J. (2002). The social impact of internet gambling. Social Science Computer Review, 20(3), 312–320.

Article  Google Scholar 

Gunstone, B., Gosschalk, K., Zabicka, E., & Sullivan-Drage, C. (2022). Annual GB Treatment and Support Survey 2021. https://www.begambleaware.org/sites/default/files/2022-03/Annual%20GB%20Treatment%20and%20Support%20Survey%20Report%202021%20%28FINAL%29.pdf

Gupta, R., Nower, L., Derevensky, J. L., Blaszczynski, A., Faregh, N., & Temcheff, C. (2013). Problem gambling in adolescents: An examination of the pathways model. Journal of Gambling Studies, 29(3), 575–588.

Article  PubMed  Google Scholar 

Haeusler, J. (2016). Follow the money: Using payment behaviour as predictor for future self-exclusion. International Gambling Studies, 16(2), 246–262.

Article  Google Scholar 

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.

Article  Google Scholar 

Hing, N., Russell, A. M., & Browne, M. (2017). Risk factors for gambling problems on online electronic gaming machines, race betting and sports betting. Frontiers in Psychology, 8, 779.

Article  PubMed  PubMed Central  Google Scholar 

Hing, N., Russell, A. M., Black, A., Rockloff, M., Browne, M., Rawat, V., & Woo, L. (2022). Gambling prevalence and gambling problems amongst land-based-only, online-only and mixed-mode gamblers in Australia: A national study. Computers in Human Behavior, 132, 107269.

Article  Google Scholar 

Holden, J. T., & Edelman, M. (2020). A short treatise on sports gambling and the law: How America regulates its most lucrative vice. Wisconsin Law Review, 907, 907–974.

Google Scholar 

Holtgraves, T. (2008). Evaluating the Problem Gambling Severity Index. Journal of Gambling Studies, 25(1), 105–120.

Article  PubMed  Google Scholar 

Hopfgartner, N., Auer, M., Griffiths, M. D., & Helic, D. (2023). Predicting self-exclusion among online gamblers: An empirical real-world study. Journal of Gambling Studies, 39(1), 447–465.

Article  PubMed  Google Scholar 

Hopley, A. A., & Nicki, R. M. (2010). Predictive factors of excessive online poker playing. Cyberpsychology, Behavior and Social Networking, 13(4), 379–385.

Article  PubMed  Google Scholar 

Icenogle, G., Steinberg, L., Duell, N., Chein, J., Chang, L., Chaudhary, N., & Bacchini, D. (2019). Adolescents’ cognitive capacity reaches adult levels prior to their psychosocial maturity: Evidence for a “maturity gap” in a multinational, cross-sectional sample. Law and Human Behavior, 43(1), 69–85.

Article  PubMed  PubMed Central  Google Scholar 

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.

Book  Google Scholar 

Kun, B., Balázs, H., Arnold, P., Paksi, B., & Demetrovics, Z. (2012). Gambling in western and eastern Europe: The example of Hungary. Journal of Gambling Studies, 28, 27–46.

Article  PubMed  Google Scholar 

Lin, E. Y. J., Casswell, S., Easton, B., Huckle, T., Asiasiga, L., & You, R. Q. (2010). Time and money spent gambling and the relationship with quality-of-life measures: A national study of New Zealanders. Journal of Gambling Issues, 24, 33–53.

Article  CAS  Google Scholar 

Louderback, E. R., LaPlante, D. A., Currie, S. R., & Nelson, S. E. (2021). Developing and validating lower risk online gambling thresholds with actual bettor data from a major internet gambling operator. Psychology of Addictive Behaviors, 35(8), 921–938.

Article  PubMed  Google Scholar 

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), 333–342.

Article  PubMed  PubMed Central  Google Scholar 

Mora-Salgueiro, J., García-Estela, A., Hogg, B., Angarita-Osorio, N., Amann, B. L., Carlbring, P., Jiménez-Murcia, S., Pérez-Sola, V., & Colom, F. (2021). The prevalence and clinical and sociodemographic factors of problem online gambling: A systematic review. Journal of Gambling Studies, 37, 899–926.

Article  PubMed  Google Scholar 

Murch, W. S., Kairouz, S., Dauphinais, S., Picard, E., Costes, J. M., & French, M. (2023). Using machine learning to retrospectively predict self-reported gambling problems in Quebec. Addiction, 118(8), 1569–1578.

Article  PubMed  Google Scholar 

留言 (0)

沒有登入
gif