Online gambling in unprecedented times: Risks and safer gambling strategies during the COVID-19 pandemic

Stark, Sasha; Robinson, Janine

doi:10.4309/jgi.2021.47.17

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…In this respect, public health organizations and trusted professionals (as opposed to inaccurate and untrustworthy information sources) should intensify communication with the general public, in order to raise awareness, caution against potential consequences of gambling, and offer timely advice on safe practices. For instance, adequate information regarding high-risk online games (with high-speed of play or unfamiliar conditions), will prevent new or inexperienced players from making uninformed and impulsive financial decisions [56]. Specialized support and digital treatment by professional addiction services should be made available for players who intensified their gambling behavior during the pandemic [54,55].…”

Section: Gamblingmentioning

confidence: 99%

Problematic use of the internet during the COVID-19 pandemic: Good practices and mental health recommendations

Gjoneska

Potenza

Jones

et al. 2022

Comprehensive Psychiatry

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Section: Gamblingmentioning

confidence: 99%

Problematic use of the internet during the COVID-19 pandemic: Good practices and mental health recommendations

Gjoneska

Potenza

Jones

et al. 2022

Comprehensive Psychiatry

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“…Our predictor variables (see Supplementary Materials Table S3. 1) were generated from data collected on lotoquebec.com (formerly espacejeux.com) during the 52 weeks preceding each participant's completion of the PGSI, and therefore, referred to the same time period as participants' survey responses. The predictor variables we created (144) using these data are summarized on Supplementary Materials Table S3.1.…”

Section: Indicators Of Gambling Behaviourmentioning

confidence: 99%

“…Online gambling activities have seen booming popularity in recent years [1,2]. Worryingly, the bulk of online gambling revenue comes from a small minority of highly-engaged users [3,4] and increases in participation may aggravate gambling-related harms.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to retrospectively predict self‐reported gambling problems in Quebec

et al. 2023

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Background and Aims: Participating in online gambling is associated with an increased risk for experiencing gambling-related harms, driving calls for more effective, personalized harm prevention initiatives. Such initiatives depend on the development of models capable of detecting at-risk online gamblers. We aimed to determine whether machine learning algorithms can use site data to detect retrospectively at-risk online gamblers indicated by the Problem Gambling Severity Index (PGSI).Design: Exploratory comparison of six prominent supervised machine learning methods (decision trees, random forests, K-nearest neighbours, logistic regressions, artificial neural networks and support vector machines) to predict problem gambling risk levels reported on the PGSI.Setting: Lotoquebec.com (formerly espacejeux.com), an online gambling platform operated by Loto-Québec (a provincial Crown Corporation) in Quebec, Canada.Participants: N = 9145 adults (18+) who completed the survey measure and placed at least one bet using real money on the site.Measurements: Participants completed the PGSI, a self-report questionnaire with validated cut-offs denoting a moderate-to-high-risk (PGSI 5+) or high-risk (PGSI 8+) for experiencing past-year gambling-related problems. Participants agreed to release additional data about the preceding 12 months from their user accounts. Predictor variables (144) were derived from users' transactions, apparent betting behaviours, listed demographics and use of responsible gambling tools on the platform.Findings: Our best classification models (random forests) for the PGSI 5+ and 8+ outcome variables accounted for 84.33% (95% CI = 82.24-86.41) and 82.52% (95% CI = 79.96-85.08) of the total area under their receiver operating characteristic curves, respectively. The most important factors in these models included the frequency and variability of participants' betting behaviour and repeat engagement on the site.Conclusions: Machine learning algorithms appear to be able to classify at-risk online gamblers using data generated from their use of online gambling platforms. They may enable personalized harm prevention initiatives, but are constrained by trade-offs between their sensitivity and precision.

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“…However, Ströbel, Stieler & Stegmann have identified what accounts for the rise of virtual sports betting among young, educated gamblers: digital development. Stark & Robinson (2021) assessed the positive impact of the COVID-19 lockdown on the development of virtual sports betting in the world. As sports and other activities were shut down during the lockdown, gamblers or bettors had no option but to turn to fantasy or virtual sports to place a bet.…”

Section: Introductionmentioning

confidence: 99%

Sports Betting Has Gone Virtual: A Sociological Analysis of a New Form of Sports Betting Among Youths in Northern Nigeria

Muhammad

Abdul-Qadir

Karofi

et al. 2022

IJSED

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The paper examined the rise of virtual sports betting among football bettors in Northern Nigeria. The study aims to examine the level of penetration of virtual betting and identify the reasons for the development. The researcher adopted Instant Gratification Model to explain the shift from betting on real-time sports betting to virtual betting in the areas selected for the study. The study has adopted a cross-sectional study design, and data were elicited through mixed methods (qualitative and quantitative). Thirteen interviews were conducted: Six Key Informant Interviews (KII) and seven In-Depth Interviews (IDI) with the sports bettors & operators of sports betting outlets. For the quantitative data, 927 structured questionnaires were administered, while 685 were retrieved from the respondents, indicating a 74% response rate. Qualitative data are presented in a prose style, while quantitative data are presented in a tabular form, of frequency tables and cross-tabulation based on the study's objectives. The study found that most virtual sports bettors were youth and educated. It also found that the availability of virtual sports betting 24 hours, unlike actual sporting events, has been identified as one factor that motivates football bettors or gamblers to shift their attention to virtual betting. Furthermore, it found that there are other virtual sporting events like Dog Race and Basketball that attract the bettors' attention to virtual betting. Additionally, the time to finish a virtual football match of fewer than three minutes is also one of the reasons that sports bettors patronize virtual betting because they do not need to wait for 90 minutes to know their fate as in an accurate football match.

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Online gambling in unprecedented times: Risks and safer gambling strategies during the COVID-19 pandemic

Cited by 12 publications

References 20 publications

Problematic use of the internet during the COVID-19 pandemic: Good practices and mental health recommendations

Problematic use of the internet during the COVID-19 pandemic: Good practices and mental health recommendations

Using machine learning to retrospectively predict self‐reported gambling problems in Quebec

Sports Betting Has Gone Virtual: A Sociological Analysis of a New Form of Sports Betting Among Youths in Northern Nigeria

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