Time series clustering of online gambling activities for addicted users’ detection

Detalhes bibliográficos
Autor(a) principal: Peres, Fernando
Data de Publicação: 2021
Outros Autores: Fallacara, Enrico, Manzoni, Luca, Castelli, Mauro, Popovič, Aleš, Rodrigues, Miguel, Estevens, Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/114698
Resumo: Peres, F., Fallacara, E., Manzoni, L., Castelli, M., Popovič, A., Rodrigues, M., & Estevens, P. (2021). Time series clustering of online gambling activities for addicted users’ detection. Applied Sciences (Switzerland), 11(5), [2397]. https://doi.org/10.3390/app11052397
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spelling Time series clustering of online gambling activities for addicted users’ detectionHuman behavior modelingMachine learningOnline gamblingMaterials Science(all)InstrumentationEngineering(all)Process Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer ProcessesPeres, F., Fallacara, E., Manzoni, L., Castelli, M., Popovič, A., Rodrigues, M., & Estevens, P. (2021). Time series clustering of online gambling activities for addicted users’ detection. Applied Sciences (Switzerland), 11(5), [2397]. https://doi.org/10.3390/app11052397Ever 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.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNPeres, FernandoFallacara, EnricoManzoni, LucaCastelli, MauroPopovič, AlešRodrigues, MiguelEstevens, Pedro2021-03-29T22:25:06Z2021-03-082021-03-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/114698eng2076-3417PURE: 28933288https://doi.org/10.3390/app11052397info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:57:25Zoai:run.unl.pt:10362/114698Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:36.915710Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Time series clustering of online gambling activities for addicted users’ detection
title Time series clustering of online gambling activities for addicted users’ detection
spellingShingle Time series clustering of online gambling activities for addicted users’ detection
Peres, Fernando
Human behavior modeling
Machine learning
Online gambling
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
title_short 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_sort Time series clustering of online gambling activities for addicted users’ detection
author Peres, Fernando
author_facet Peres, Fernando
Fallacara, Enrico
Manzoni, Luca
Castelli, Mauro
Popovič, Aleš
Rodrigues, Miguel
Estevens, Pedro
author_role author
author2 Fallacara, Enrico
Manzoni, Luca
Castelli, Mauro
Popovič, Aleš
Rodrigues, Miguel
Estevens, Pedro
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Peres, Fernando
Fallacara, Enrico
Manzoni, Luca
Castelli, Mauro
Popovič, Aleš
Rodrigues, Miguel
Estevens, Pedro
dc.subject.por.fl_str_mv Human behavior modeling
Machine learning
Online gambling
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
topic Human behavior modeling
Machine learning
Online gambling
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
description Peres, F., Fallacara, E., Manzoni, L., Castelli, M., Popovič, A., Rodrigues, M., & Estevens, P. (2021). Time series clustering of online gambling activities for addicted users’ detection. Applied Sciences (Switzerland), 11(5), [2397]. https://doi.org/10.3390/app11052397
publishDate 2021
dc.date.none.fl_str_mv 2021-03-29T22:25:06Z
2021-03-08
2021-03-08T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/114698
url http://hdl.handle.net/10362/114698
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2076-3417
PURE: 28933288
https://doi.org/10.3390/app11052397
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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