# Time Series Clustering of Online Gambling Activities for Addicted Users' Detection
###### Abstract  
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. human behavior modeling; online gambling; machine learning +
Footnote †: journal: Medical Science  
0000-0001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8 substance abuse) and psychosocial maladjustment (suicidality, financial difficulties, and reduced social support). These findings reinforce the reality of that addiction as a public health concern. Another study [5] analyzed the financial, productivity-related, personal, and family costs associated with online gambling; it showed that these costs are generally greater than gambling taxation income. This is to say that a gambling addiction tends to have a detrimental effect on public finances. In 2012, the European Commission released a statement [1] highlighting the need for regulatory policies to aid in the detection of pathological gambling behaviors, citing \"a responsibility to protect those citizens and families who suffer from a gambling addiction.\" Despite this earnest recommendation, research aimed at developing a coherent set of responsible national policies has yet to make it past the embryonic stage. In Portugal, the Gambling Inspection and Regulation Service is \"responsible for the control and regulation of gambling activities in casinos and bingo halls, as well as online gambling and betting.\" To comply with operational and regulatory objectives, this authority receives, on a daily basis, all data related to online gambling activities pursued by every user on every online platform with services that are accessible to Portuguese citizens. In spite of this prodigious collection of data, the authority still lacks appropriate tools for identifying gambling addicts. This authority acknowledges a profound scarcity of actionable data regarding the actual scope of gambling addiction and a consequent lack of expertise about how best to deal with this problem. The same authority observes that \"the human dimension and economical and social relevance of this issue (i.e., gambling addiction) demands scientific studies.\"  
To answer this call, this paper proposes an ML (Machine Learning)-based tool that could capitalize on the vast amount of data collected every day and analyze online user behavior to model and detect the behaviors associated with addicted gamblers. The problem represents a major challenge due the massive amount of data involved. To tackle this problem, we use a clustering algorithm specifically developed for time series, namely dynamic time warping, and we show its suitability in identifying different subsets of gamblers that are characterized by a specific behavior. In particular, DTW can identify a subset of gamblers characterized by uncommon behaviors (i.e., a significant amount of money spent, number of accesses to the gambling provider platforms, etc. ), thus simplifying the subsequent work of the gambling authority.