Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2397 |
| _version_ | 1797412309862187008 |
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| author | Fernando Peres Enrico Fallacara Luca Manzoni Mauro Castelli Aleš Popovič Miguel Rodrigues Pedro Estevens |
| author_facet | Fernando Peres Enrico Fallacara Luca Manzoni Mauro Castelli Aleš Popovič Miguel Rodrigues Pedro Estevens |
| author_sort | Fernando Peres |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:00:18Z |
| format | Article |
| id | doaj.art-9df6462fef80421396305d8363beeed8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:00:18Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9df6462fef80421396305d8363beeed82023-12-03T13:01:04ZengMDPI AGApplied Sciences2076-34172021-03-01115239710.3390/app11052397Time Series Clustering of Online Gambling Activities for Addicted Users’ DetectionFernando Peres0Enrico Fallacara1Luca Manzoni2Mauro Castelli3Aleš Popovič4Miguel Rodrigues5Pedro Estevens6Nova Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalDipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Via Valerio 12/1, 34127 Trieste, ItalyDipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Via Valerio 12/1, 34127 Trieste, ItalyNova Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalNova Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalSRIJ, Serviço de Regulação e Inspeção de Jogos, Rua Ivone Silva, 1050-124 Lisboa, PortugalSRIJ, Serviço de Regulação e Inspeção de Jogos, Rua Ivone Silva, 1050-124 Lisboa, PortugalEver 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.https://www.mdpi.com/2076-3417/11/5/2397human behavior modelingonline gamblingmachine learning |
| spellingShingle | Fernando Peres Enrico Fallacara Luca Manzoni Mauro Castelli Aleš Popovič Miguel Rodrigues Pedro Estevens Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection Applied Sciences human behavior modeling online gambling machine learning |
| title | Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection |
| title_full | Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection |
| title_fullStr | Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection |
| title_full_unstemmed | Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection |
| title_short | Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection |
| title_sort | time series clustering of online gambling activities for addicted users detection |
| topic | human behavior modeling online gambling machine learning |
| url | https://www.mdpi.com/2076-3417/11/5/2397 |
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