Persuasive Design and Algorithms in Online Betting Platforms

Ethical, Communicative, and Public Health Implications

Authors

DOI:

https://doi.org/10.62701/revsocial.v13.5497

Keywords:

Personalization algorithms, Artificial intelligence, Online gambling, Persuasive design, Dark patterns, Algorithmic communication, Ethical digital regulation

Abstract

This study examines how artificial intelligence algorithms, personalization, and persuasive design shape user experiences on online gambling platforms. Through a systematic literature review (2015–2025), it identifies practices such as behavioral segmentation, dark patterns, and algorithmic communication aimed at maximizing user retention. Findings reveal a technocommunicative ecosystem that is opaque, emotionally manipulative, and underregulated, posing significant risks to user autonomy. The study calls for enhanced transparency, critical digital literacy, and ethical regulation as key strategies to mitigate these effects.

Downloads

Download data is not yet available.

Global Statistics ℹ️

Cumulative totals since publication
25
Views
4
Downloads
29
Total

References

Ahmad, N., y Aurangzeb, M. (2025). Revolutionizing Casino Operations: The Role of Artificial Intelligence and Big Data in Enhancing Customer Loyalty and Revenue Growth. https://doi.org/10.13140/RG.2.2.31978.43209

Andersson, S., Carlbring, P., Lyon, K., Bermell, M., & Lindner, P. (2025). Insights into the temporal dynamics of identifying problem gambling on an online casino: A machine learning study on routinely collected individual account data. Journal of Behavioral Addictions, 14(1), 490–500. https://doi.org/10.1556/2006.2025.00013 DOI: https://doi.org/10.1556/2006.2025.00013

Aonso-Diego, G., García-Pérez, Á., y Krotter, A. (2025). Impact of Spanish gambling regulations on online gambling behavior and marketing strategies. Harm Reduction Journal, 22(1), 1-12. https://link.springer.com/article/10.1186/s12954-025-01219-7 DOI: https://doi.org/10.1186/s12954-025-01219-7

Auer, M. M., y Griffiths, M. D. (2015). The use of personalized behavioral feedback for online gamblers: an empirical study. Frontiers in psychology, 6, 1406. DOI: https://doi.org/10.3389/fpsyg.2015.01406

Boerman, S. C., Kruikemeier, S., y Zuiderveen Borgesius, F. J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of advertising, 46(3), 363-376. https://doi.org/10.1080/00913367.2017.1339368 DOI: https://doi.org/10.1080/00913367.2017.1339368

Braun, V., y Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa DOI: https://doi.org/10.1191/1478088706qp063oa

Brehm, J. W. (1966). A theory of psychological reactance. Academic Press.

Cortés Torres, J. E., Saldaña Moreno, C. E., Mendoza Moncada, J. S., y Perdomo Pineda, J. D. (2024). El chatbot aplicado a salud. Una revisión bibliométrica. Revista de Comunicación y Salud, 15, e355. https://doi.org/10.35669/rcys.2025.15.e355 DOI: https://doi.org/10.35669/rcys.2025.15.e355

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., y Vayena, E. (2018). AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and machines, 28, 689-707. https://doi.org/10.1007/s11023-018-9482-5 DOI: https://doi.org/10.1007/s11023-018-9482-5

Gainsbury, S., Hing, N., Delfabbro, P. H., y King, D. L. (2014). A taxonomy of gambling and casino games via social media and online technologies. International gambling studies, 14(2), 196-213. https://doi.org/10.1080/14459795.2014.890634 DOI: https://doi.org/10.1080/14459795.2014.890634

Gray, C. M., Kou, Y., Battles, B., Hoggatt, J., y Toombs, A. L. (2018, April). The dark (patterns) side of UX design. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-14). https://doi.org/10.1145/3173574.3174108 DOI: https://doi.org/10.1145/3173574.3174108

Griffiths, M. D. (2019). The psychology of gambling. Routledge.

Helberger, N., Pierson, J., y Poell, T. (2018). Governing online platforms: From contested to cooperative responsibility. The information society, 34(1), 1-14. https://doi.org/10.1080/01972243.2017.1391913 DOI: https://doi.org/10.1080/01972243.2017.1391913

Kairouz, S., Costes, J. M., Murch, W. S., Doray-Demers, P., Carrier, C., y Eroukmanoff, V. (2023). Enabling new strategies to prevent problematic online gambling: A machine learning approach for identifying at-risk online gamblers in France. International Gambling Studies, 23(3), 471-490. https://doi.org/10.1080/14459795.2022.2164042 DOI: https://doi.org/10.1080/14459795.2022.2164042

Kaptein, M., y Eckles, D. (2012). Heterogeneity in the effects of online persuasion. Journal of Interactive Marketing, 26(3), 176-188. https://doi.org/10.1016/j.intmar.2012.02.002 DOI: https://doi.org/10.1016/j.intmar.2012.02.002

Kaptein, M., Lacroix, J., y Saini, P. (2015). Individual differences in persuasive technology design: Tailoring persuasive messages using persuasion profiles. International Journal of Human-Computer Studies, 77, 38–51. https://doi.org/10.1016/j.ijhcs.2015.01.004 DOI: https://doi.org/10.1016/j.ijhcs.2015.01.004

Kim, J., y Jeong, H. J. (2023). «It’s my virtual space»: the effect of personalized advertising within social media. International Journal of Advertising, 42(8), 1267-1294. https://doi.org/10.1080/02650487.2023.2274243 DOI: https://doi.org/10.1080/02650487.2023.2274243

Kollmer, T., y Eckhardt, A. (2023). Dark patterns: conceptualization and future research directions. Business y information systems engineering, 65(2), 201-208. https://doi.org/10.1007/s12599-022-00783-7 DOI: https://doi.org/10.1007/s12599-022-00783-7

Lopez-Gonzalez, H., Estévez, A., y Griffiths, M. D. (2018). Internet-based structural characteristics of sports betting and problem gambling severity: Is there a relationship? Journal of Behavioral Addictions, 7(2), 423–431. https://doi.org/10.1556/2006.7.2018.49 DOI: https://doi.org/10.1556/2006.7.2018.49

Luguri, J. B., y Strahilevitz, L. (2021). Shining a light on dark patterns. Journal of Legal Analysis, 13, 43–109. https://doi.org/10.1093/jla/laaa006 DOI: https://doi.org/10.1093/jla/laaa006

Lyell, D., y Coiera, E. (2017). Automation bias and verification complexity: a systematic review. Journal of the American Medical Informatics Association, 24(2), 423-431. https://doi.org/10.1093/jamia/ocw105 DOI: https://doi.org/10.1093/jamia/ocw105

Martin, K. D., y Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45, 135–155. https://doi.org/10.1007/s11747-016-0495-4 DOI: https://doi.org/10.1007/s11747-016-0495-4

Mathur, A., Acar, G., Friedman, M. G., Lucherini, E., Mayer, J., Chetty, M., y Narayanan, A. (2019). Dark patterns at scale: Findings from a crawl of 11K shopping websites. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–32. https://doi.org/10.1145/3359183 DOI: https://doi.org/10.1145/3359183

Matos Agudo, D., Rubio Gil, F. J., Nieto Manibardo, E., Rey García, P., y Gómez Sánchez, J. C. (2024). Estudio cualitativo de los intercambios comunicativos en la asistencia sanitaria hospitalaria a través de los estudiantes del grado de Medicina. Revista de Comunicación y Salud, 15, e373. https://doi.org/10.35669/rcys.2025.15.e373 DOI: https://doi.org/10.35669/rcys.2025.15.e373

McGrane, E., Pryce, R., Field, M., Gu, S., Moore, E. C., y Goyder, E. (2025). What is the impact of sports‐related gambling advertising on gambling behaviour? A systematic review. Addiction, 120(4), 589-607. https://doi.org/10.1111/add.16761 DOI: https://doi.org/10.1111/add.16761

Min, M., y Lee, D. A. (2024). Illegal online gambling site detection using multiple resource-oriented machine learning. Journal of Gambling Studies, 40(4), 2237–2255. https://doi.org/10.1007/s10899-024-10337-z DOI: https://doi.org/10.1007/s10899-024-10337-z

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., y Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data y Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679 DOI: https://doi.org/10.1177/2053951716679679

Newall, P., Weiss-Cohen, L., Torrance, J., y Bart, Y. (2024). Not always as advertised: different effects from viewing safer gambling adverts on gambling urges. OSF. https://bit.ly/4lqcFtj DOI: https://doi.org/10.31219/osf.io/8tpqf

Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253.

https://doi.org/10.1518/001872097778543886 DOI: https://doi.org/10.1518/001872097778543886

Parke, J., Wardle, H., Rigbye, J., y Parke, A. (2013). Exploring social gambling: Scoping, classification and evidence review. Gambling Commission. https://doi.org/10.2139/ssrn.2510435

Petty, R. E., y Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Springer. DOI: https://doi.org/10.1007/978-1-4612-4964-1_1

Pfund, R. A., Ginley, M. K., Kim, H. S., Boness, C. L., Horn, T. L., y Whelan, J. P. (2023). Cognitive-behavioral treatment for gambling harm: Umbrella review and meta-analysis. Clinical psychology review, 105, 102336. https://doi.org/10.1016/j.cpr.2023.102336 DOI: https://doi.org/10.1016/j.cpr.2023.102336

Rossi, R., y Nairn, A. (2024). Priming young minds: The appeal of gambling advertising to children and young people. Journal of the Association for Consumer Research, 9(2), 187-199. https://doi.org/10.1086/729290 DOI: https://doi.org/10.1086/729290

Seo, W., Kim, N., Lee, S. K., y 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 DOI: https://doi.org/10.1556/2006.2020.00063

Sumner, P., Vivian-Griffiths, S., Boivin, J., Williams, A., Bott, L., Adams, R., Venetis, C. A., Whelan, L., Hughes, B., y Chambers, C. D. (2016). Exaggerations and caveats in press releases and health-related science news. PloS one, 11(12), e0168217. https://doi.org/10.1371/journal.pone.0168217 DOI: https://doi.org/10.1371/journal.pone.0168217

Susser, D., Roessler, B., y Nissenbaum, H. (2019). Online manipulation: Hidden influences in a digital world. Georgetown Law Technology Review, 4, 1–45. https://doi.org/10.2139/ssrn.3306006 DOI: https://doi.org/10.2139/ssrn.3306006

Turow, J., Draper, N., Einstein, M., Hamilton, J. F., y Timke, E. (2021). The voice catchers: How marketers listen in to exploit your feelings, your privacy, and your wallet. Advertising y Society Quarterly, 22(4). https://dx.doi.org/10.1353/asr.2021.0046. DOI: https://doi.org/10.1353/asr.2021.0046

Van Schalkwyk, M. C., Petticrew, M., Cassidy, R., Adams, P., McKee, M., Reynolds, J., y Orford, J. (2021). A public health approach to gambling regulation: countering powerful influences. The Lancet Public Health, 6(8), e614-e619. https://dx.doi.org/10.1016/S2468-2667(21)00098-0 DOI: https://doi.org/10.1016/S2468-2667(21)00098-0

Zuboff, S. (2023). The age of surveillance capitalism. En Social theory re-wired: (pp. 203-213). Routledge. DOI: https://doi.org/10.4324/9781003320609-27

Published

22-12-2025

How to Cite

Martínez Martínez, L., Arteaga Ros, M., & Cuesta Cambra, U. (2025). Persuasive Design and Algorithms in Online Betting Platforms: Ethical, Communicative, and Public Health Implications. SOCIAL REVIEW. International Social Sciences Review Revista Internacional De Ciencias Sociales, 13(2), 1–16. https://doi.org/10.62701/revsocial.v13.5497

Issue

Section

Research articles