Time series clustering of online gambling activities for addicted users’ detection
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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:RCAAP2024-05-22T17:51:33Zoai:run.unl.pt:10362/114698Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:51:33Repositório 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 |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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 |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817545788069249024 |