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Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection

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Nova Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
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Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Via Valerio 12/1, 34127 Trieste, Italy
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Faculty of Economics, University of Ljubljana. Kardeljeva Ploščad 17, 1000 Ljubljana, Slovenia
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SRIJ, Serviço de Regulação e Inspeção de Jogos, Rua Ivone Silva, 1050-124 Lisboa, Portugal
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Author to whom correspondence should be addressed.
Academic Editor: Luis Javier Garcia Villalba
Appl. Sci. 2021, 11(5), 2397; https://doi.org/10.3390/app11052397
Received: 2 February 2021 / Revised: 22 February 2021 / Accepted: 24 February 2021 / Published: 8 March 2021
(This article belongs to the Section Computing and Artificial Intelligence)
Ever since the worldwide demand for gambling services started to spread, its expansion has continued steadily. To wit, online gambling is a major industry in every European country, generating billions of Euros in revenue for commercial actors and governments alike. Despite such evidently beneficial effects, online gambling is ultimately a vast social experiment with potentially disastrous social and personal consequences that could result in an overall deterioration of social and familial relationships. Despite the relevance of this problem in society, there is a lack of tools for characterizing the behavior of online gamblers based on the data that are collected daily by betting platforms. This paper uses a time series clustering algorithm that can help decision-makers in identifying behaviors associated with potential pathological gamblers. In particular, experimental results obtained by analyzing sports event bets and black jack data demonstrate the suitability of the proposed method in detecting critical (i.e., pathological) players. This algorithm is the first component of a system developed in collaboration with the Portuguese authority for the control of betting activities. View Full-Text
Keywords: human behavior modeling; online gambling; machine learning human behavior modeling; online gambling; machine learning
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MDPI and ACS Style

Peres, F.; Fallacara, E.; Manzoni, L.; Castelli, M.; Popovič, A.; Rodrigues, M.; Estevens, P. Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection. Appl. Sci. 2021, 11, 2397. https://doi.org/10.3390/app11052397

AMA Style

Peres F, Fallacara E, Manzoni L, Castelli M, Popovič A, Rodrigues M, Estevens P. Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection. Applied Sciences. 2021; 11(5):2397. https://doi.org/10.3390/app11052397

Chicago/Turabian Style

Peres, Fernando; Fallacara, Enrico; Manzoni, Luca; Castelli, Mauro; Popovič, Aleš; Rodrigues, Miguel; Estevens, Pedro. 2021. "Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection" Appl. Sci. 11, no. 5: 2397. https://doi.org/10.3390/app11052397

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