Applications of data science for responsible gambling: a scoping review

References

• Adami, N., Benini, S., Boschetti, A., Canini, L., Maione, F., & Temporin, M. (2013). Markers of unsustainable gambling for early detection of at-risk online gamblers. International Gambling Studies, 13(2), 188–204. https://doi.org/10.1080/14459795.2012.754919
   Web of Science ®Google Scholar
• Akhter, S. (2018). Using machine learning to predict potential online gambling addicts [ Master’s thesis]. Aalto University. Aaltodoc. https://aaltodoc.aalto.fi/handle/123456789/34354
   Google Scholar
• Aro, D., Jakob, L., & Gainsbury, D. S. (2015). Ontario Problem Gambling Research Centre (OPGRC). Final Report, 50. https://www.greo.ca/Modules/EvidenceCentre/files/Aro%20et%20al2015Use_of_data_analytics_for_responsible_gambling_regulatory_directives.pdf
   Google Scholar
• Auer, M., & Griffiths, M. D. (2019). Predicting limit-setting behavior of gamblers using machine learning algorithms: A real-world study of Norwegian gamblers using account data. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-019-00166-2
   Web of Science ®Google Scholar
• Auer, M., & Griffiths, M. D. (2020). The use of personalized messages on wagering behavior of Swedish online gamblers: An empirical study. Computers in Human Behavior, 110, 106402. https://doi.org/10.1016/j.chb.2020.106402
   Web of Science ®Google Scholar
• Auer, M., Malischnig, D., & Griffiths, M. D. (2020). Gambling Before and During the COVID-19 Pandemic Among European Regular Sports Bettors: An Empirical Study Using Behavioral Tracking Data. Int J Ment Health Addiction. https://doi.org/10.1007/s11469-020-00327-8
   PubMed Web of Science ®Google Scholar
• Basile, A., Chinea-Rios, M., Uban, A.-S., Müller, T., Rössler, L., Yenikent, S., Chulví, B., Rosso, P., & Franco-Salvador, M. (2021). UPV-Symanto at eRisk 2021: Mental health author profiling for early risk prediction on the internet. In Working Notes of CLEF 2021-Conference and Labs of the Evaluation Forum, Bucarest, Romania. http://ceur-ws.org/Vol-2936/paper-75.pdf
   Google Scholar
• Batool, F., & Hennig, C. (2021). Clustering with the Average Silhouette Width. Computational Statistics & Data Analysis, 158, 107190. https://doi.org/10.1016/j.csda.2021.107190
   Web of Science ®Google Scholar
• Benedan, L., & Monti, G. S. (2021). Predicting the risk of gambling activities in adolescence: a case study. In P. Mariani & M. Zenga (Eds.), Data science and social research II (pp. 47–57). Springer International Publishing. https://doi.org/10.1007/978-3-030-51222-4_5
   Google Scholar
• Bermell, M. (2019). Identification of Problem Gambling via Recurrent Neural Networks: Predicting self-exclusion due to problem gambling within the remote gambling sector by means of recurrent neural networks [ Master’s thesis]. Umeå University. Digitala Vetenskapliga Arkivet. http://www.diva-portal.org/smash/record.jsf?dswid=8866&pid=diva2%3A1316538
   Google Scholar
• Bowman, C. E., Brown, R. C., Grindrod, P., & Wardle, H. (2020). Online Gambling: Hidden Markov Models for Behavioural Changes. https://www.researchgate.net/profile/Peter-Grindrod/publication/341271000_Online_Gambling_Hidden_Markov_Models_for_Behavioural_Changes/links/5eb6c5a6a6fdcc1f1dcb0fb2/Online-Gambling-Hidden-Markov-Models-for-Behavioural-Changes.pdf
   Google Scholar
• Braverman, J., LaPlante, D. A., Nelson, S. E., & Shaffer, H. J. (2013). Using cross-game behavioral markers for early identification of high-risk internet gamblers. Psychology of Addictive Behaviors, 27(3), 868–877. https://doi.org/10.1037/a0032818
   PubMed Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Bucur, A. M., Cosma, A., & Dinu, L. P. (2021). Early risk detection of pathological gambling, self-harm and depression using BERT. ArXiv:2106.16175 [Cs]. http://arxiv.org/abs/2106.16175
   Google Scholar
• Catania, M., & Griffiths, M. D. (2021). Applying the DSM-5 criteria for gambling disorder to online gambling account-based tracking data: an empirical study utilizing cluster analysis. Journal of Gambling Studies. https://doi.org/10.1007/s10899-021-10080-9
   PubMed Web of Science ®Google Scholar
• Chagas, B. T., & Gomes, J. F. (2017). Internet gambling: A critical review of behavioural tracking research. Journal of Gambling Issues, 36. https://doi.org/10.4309/jgi.2017.36.1
   Google Scholar
• Chagas, B. T., Gomes, J. F. S., & Griffiths, M. D. (2021). Consumer profile segmentation in online lottery gambling utilizing behavioral tracking data from the Portuguese National Lottery. Journal of Gambling Studies, 38(3), 917–939. https://doi.org/10.1007/s10899-021-10072-9
   PubMed Web of Science ®Google Scholar
• Challet-Bouju, G., Hardouin, J.-B., Thiabaud, E., Saillard, A., Donnio, Y., Grall-Bronnec, M., & Perrot, B. (2020). Modeling early gambling behavior using indicators from online lottery gambling tracking data: Longitudinal analysis. Journal of Medical Internet Research, 22(8), e17675. https://doi.org/10.2196/17675
   PubMed Web of Science ®Google Scholar
• Deng, X., Lesch, T., & Clark, L. (2019). Applying data science to behavioral analysis of online gambling. Current Addiction Reports, 6(3), 159–164. https://doi.org/10.1007/s40429-019-00269-9
   Google Scholar
• Division on Addiction. (2020). The Transparency Project. http://www.thetransparencyproject.org/
   Google Scholar
• Dragicevic, S., Tsogas, G., & Kudic, A. (2011). Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection. International Gambling Studies, 11(3), 377–391. https://doi.org/10.1080/14459795.2011.629204
   Web of Science ®Google Scholar
• Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14(1), 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037
   PubMed Web of Science ®Google Scholar
• Excell, D., Bobashev, G., Gonzalez-Ordonez, D., Wardle, H., Whitehead, T., Morris, R. J., & Ruddle, P. (2014). Report 3: Predicting problem gamblers: analysis of industry data.
   Google Scholar
• 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. https://doi.org/10.1080/14459795.2019.1643901
   Web of Science ®Google Scholar
• Forsström, D., Spångberg, J., Petterson, A., Brolund, A., & Odeberg, J. (2020). A systematic review of educational programs and consumer protection measures for gambling: An extension of previous reviews. Addiction Research & Theory, 29(5), 398–412.
   Web of Science ®Google Scholar
• Gainsbury, S. M., & Blaszczynski, A. (2020). Digital gambling payment methods: Harm minimization policy considerations. Gaming Law Review, 24(7), 466–472. https://doi.org/10.1089/glr2.2020.0015
   Google Scholar
• Garcia Marquez, F. P. (Ed.). (2020). Handbook of research on big data clustering and machine learning. IGI Global. https://doi.org/10.4018/978-1-7998-0106-1
   Google Scholar
• Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
   PubMed Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2018). Multivariate data analysis (8th ed.). Cengage Learning.
   Google Scholar
• Hassanniakalager, A., & Newall, P. W. (2019). A machine learning perspective on responsible gambling. Behavioural Public Policy, 6(2), 1–24. https://doi.org/10.1017/bpp.2019.9
   Google Scholar
• Hechler, E., Oberhofer, M., & Schaeck, T. (2020). The operationalization of AI. In Deploying AI in the Enterprise (pp. 115–140). Apress.
   Google Scholar
• Hennig, C. (2015). What are the true clusters? Pattern Recognition Letters, 64, 53–62. https://doi.org/10.1016/j.patrec.2015.04.009
   Web of Science ®Google Scholar
• Ivanova, E., Lindner, P., & Carlbring, P. (2019). Using the random forest algorithm on customer gambling data for predicting gambling freezes in an online gambling platform. Digitala Vetenskapliga Arkivet. http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Asu%3Adiva-176229
   Google Scholar
• Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
   PubMed Web of Science ®Google Scholar
• Kotthoff, L. (2016). Algorithm selection for combinatorial search problems: a survey. In C. Bessiere, L. De Raedt, L. Kotthoff, S. Nijssen, B. O’Sullivan, & D. Pedreschi (Eds.), Data mining and constraint programming. Lecture notes in computer science (pp. 149–190). Springer. https://doi.org/10.1007/978-3-319-50137-6_7
   Google Scholar
• Lee, I., & Shin, Y. J. (2020). Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), 157–170. https://doi.org/10.1016/j.bushor.2019.10.005
   Web of Science ®Google Scholar
• Lopes, R. P. (2021). CeDRI at eRisk 2021: A naive approach to early detection of psychological disorders in social media. In Working Notes of CLEF 2021-Conference and Labs of the Evaluation Forum, Bucarest, Romania. http://hdl.handle.net/10198/24037
   Google Scholar
• Loyola, J. M., Burdisso, S., Thompson, H., Cagnina, L., & Errecalde, M. (2021). UNSL at eRisk 2021: A comparison of three early alert policies for early risk detection. In Working Notes of CLEF 2021-Conference and Labs of the Evaluation Forum, Bucarest, Romania. http://ceur-ws.org/Vol-2936/paper-81.pdf
   Google Scholar
• Luquiens, A., Tanguy, M.-L., Benyamina, A., Lagadec, M., Aubin, H.-J., & Reynaud, M. (2016). Tracking online poker PGs with player account-based gambling data only. International Journal of Methods in Psychiatric Research, 25(4), 333–342. https://doi.org/10.1002/mpr.1510
   PubMed Web of Science ®Google Scholar
• 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), e022541. https://doi.org/10.1136/bmjopen-2018-022541
   PubMed Web of Science ®Google Scholar
• Mahood, Q., Van Eerd, D., & Irvin, E. (2014). Searching for grey literature for systematic reviews: Challenges and benefits. Research Synthesis Methods, 5(3), 221–234. https://doi.org/10.1002/jrsm.1106
   PubMed Web of Science ®Google Scholar
• Mak, K. K., Lee, K., & Park, C. (2019). Applications of machine learning in addiction studies: A systematic review. Psychiatry Research, 275, 53–60. https://doi.org/10.1016/j.psychres.2019.03.001
   PubMed Web of Science ®Google Scholar
• Mankins, J. C. (2009). Technology readiness assessments: A retrospective. Acta Astronautica, 65(9–10), 1216–1223. https://doi.org/10.1016/j.actaastro.2009.03.058
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Maupomé, D., Armstrong, M. D., Rancourt, F., Soulas, T., & Meurs, M. J. (2021). Early detection of signs of pathological gambling, self-harm and depression through topic extraction and neural networks. In Working Notes of CLEF 2021-Conference and Labs of the Evaluation Forum, Bucarest, Romania. http://ceur-ws.org/Vol-2936/paper-83.pdf
   Google Scholar
• McNair, S., Chonaire, A. N., Barry, R., Leak-Smith, L., Larkin, C., Loke, T., & Lawrence, J. (2021, July 22). Dealing new data: what bank transactions can tell us about gambling behaviour. The Behavioural Insights Team. https://www.bi.team/blogs/dealing-new-data-what-bank-transactions-can-tell-us-about-gambling-behaviour/
   Google Scholar
• Mijić, D., & Varga, E. (2017). Machine learning driven responsible gaming framework with apache spark. In 2017 25th Telecommunication Forum (TELFOR) (pp. 1–4). IEEE. https://doi.org/10.1109/TELFOR.2017.8249466
   Google Scholar
• Mosquera, M. G., & Keselj, V. (2017). Identifying electronic gaming machine gambling personae through unsupervised session classification. Big Data & Information Analytics, 2(2), 141. https://doi.org/10.3934/bdia.2017015
   Google Scholar
• Muggleton, N., Parpart, P., Newall, P., Leake, D., Gathergood, J., & Stewart, N. (2021). The association between gambling and financial, social and health outcomes in big financial data. Nature Human Behaviour, 5(3), 319–326. https://doi.org/10.1038/s41562-020-01045-w
   PubMed Web of Science ®Google Scholar
• Nevada Gaming Commission. (2020). Disposition June 2020 Agenda. https://gaming.nv.gov/modules/showdocument.aspx?documentid=16859
   Google Scholar
• Nevada Gaming Commission. (2020b). Adoption of Amendments to Regulations of the Nevada Gaming Control Board and Nevada Gaming Commission. https://gaming.nv.gov/modules/showdocument.aspx?documentid=16869
   Google Scholar
• Parodi, S., Dosi, C., Zambon, A., Ferrari, E., & Muselli, M. (2017). Identifying environmental and social factors predisposing to pathological gambling combining standard logistic regression and logic learning machine. Journal of Gambling Studies, 33(4), 1121–1137. https://doi.org/10.1007/s10899-017-9679-1
   PubMed Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Percy, C., Garcez, A. S. D., Dragičević, S., França, M. V., Slabaugh, G., & Weyde, T. (2016b). The need for knowledge extraction: Understanding harmful gambling behavior with neural networks. In Proceedings of the Twenty-Second European Conference on Artificial Intelligence, (pp. 974–981). IOS Press. https://doi.org/10.3233/978-1-61499-672-9-974
   Google Scholar
• Peres, F., Fallacara, E., Manzoni, L., Castelli, M., Popovic, A., Rodrigues, M., & Estevens, P. (2021). Time series clustering of online gambling activities for addicted users’ detection. Applied Sciences-Basel, 11(5), 2397. https://doi.org/10.3390/app11052397
   Web of Science ®Google Scholar
• Perrot, B., Hardouin, J. B., Costes, J. M., Caillon, J., Grall-Bronnec, M., & Challet-Bouju, G. (2017). Study protocol for a transversal study to develop a screening model for excessive gambling behaviours on a representative sample of users of French authorised gambling websites. BMJ Open, 7(5), e014600. https://doi.org/10.1136/bmjopen-2016-014600
   PubMed Web of Science ®Google Scholar
• Perrot, B., Hardouin, J., Grall‐bronnec, M., & Challet‐bouju, G. (2018). Typology of online lotteries and scratch games gamblers’ behaviours: A multilevel latent class cluster analysis applied to player account‐based gambling data. International Journal of Methods in Psychiatric Research, 27(4). https://doi.org/10.1002/mpr.1746
   PubMed Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• PricewaterhouseCoopers LLP. (2017, August). Remote gambling research interim report on phase II. https://www.begambleaware.org/media/1549/gamble-aware_remote-gambling-research_phase-2_pwc-report_august-2017-final.pdf
   Google Scholar
• Sarkar, S., Weyde, T., Garcez, A., Slabaugh, G. G., Dragicevic, S., & Percy, C. (2016, December). Accuracy and interpretability trade-offs in machine learning applied to safer gambling. In CEUR Workshop Proceedings. http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper10.pdf
   Google Scholar
• Seo, W., Kim, N., Lee, S. K., & Park, S. M. (2020). Machine learning-based analysis of adolescent gambling factors. Journal of Behavioral Addictions, 9(3), 734–743. https://doi.org/10.1556/2006.2020.00063
   PubMed Web of Science ®Google Scholar
• Sharing high expectations. (2019). Nature machine intelligence. 1(8), 329. https://doi.org/10.1038/s42256-019-0092-6
   Google Scholar
• Sikiric, K. (2020). Gambling safety net: Predicting the risk of problem gambling using Bayesian networks [ Master’s thesis]. Linköping University. Digitala Vetenskapliga Arkivet. http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Aliu%3Adiva-165867
   Google Scholar
• Silva, V. (2019). A Flexible Framework to Monitor Evolution of Clusters [ Master’s thesis]. University of Alberta. Education & Research Archive.
   Google Scholar
• 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. https://doi.org/10.4309/jgi.2021.47.17
   Web of Science ®Google Scholar
• Suriadi, S., Susnjak, T., Ponder-Sutton, A., Watters, P., & Schumacher, C. (2016). Using data-driven and process mining techniques for identifying and characterizing PGs in New Zealand. Complex Systems Informatics and Modeling Quarterly, 9(9), 44–66. https://doi.org/10.7250/csimq.2016-9.03
   Google Scholar
• Suzuki, H., Nakamura, R., Inagaki, A., Watanabe, I., & Takagi, T. (2019). Early detection of problem gambling based on behavioral changes using shapelets. 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 367–372. https://doi.org/10.1145/3350546.3352549
   Google Scholar
• Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhou, N. H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C. … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/m18-0850
   PubMed Web of Science ®Google Scholar
• Ukhov, I., Bjurgert, J., Auer, M., & Griffiths, M. D. (2021). Online problem gambling: A comparison of casino players and sports bettors via predictive modeling using behavioral tracking data. Journal of Gambling Studies, 37(3), 877–897. https://doi.org/10.1007/s10899-020-09964-z
   PubMed Web of Science ®Google Scholar
• Virginia’s Legislative Information System. (2020). HB 896 Virginia Lottery; powers and duties of Board, sports betting, etc. https://lis.virginia.gov/cgi-bin/legp604.exe?201+sum+HB896
   Google Scholar
• Whitaker, S. D. (2020). How much help do state and local governments need? Updated estimates of revenue losses from pandemic mitigation. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-ddb-20200629
   Google Scholar
• Wiley, R. C., Tom, M. A., Edson, T. C., & LaPlante, D. A. (2020). Behavioral markers of risky daily fantasy sports play. Journal of Sport and Social Issues, 44(4), 356–371. https://doi.org/10.1177/0193723520919819
   Web of Science ®Google Scholar
• Wing, J. M. (2019). The data life cycle. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.e26845b4
   Google Scholar