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

Fernando Peres, Enrico Fallacara, Luca Manzoni, Mauro Castelli, Aleš Popovič, Miguel Rodrigues, Pedro Estevens

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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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.

Original languageEnglish
Article number2397
JournalApplied Sciences (Switzerland)
Issue number5
Publication statusPublished - 8 Mar 2021


  • Human behavior modeling
  • Machine learning
  • Online gambling


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