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Back tracking: A brief look at using big data in gambling research

I’ve been working in the area of gambling for nearly 30 years and over the past 15 years I have carrying out research into both online gambling and responsible gambling. As I have outlined in previous blogs, one of the new methods I have been using in my published papers is online behavioural tracking. The chance to carry out innovative research in both areas using a new methodology was highly appealing – especially as I have used so many other methods in my gambling research (including online and offline surveys, experiments in laboratories and ecologically valid settings, offline focus groups, online and offline case study interviews, participant and non-participation observation, secondary analysis of survey data, and analysis of various forms of online data such as those found in online forums and online diary blogs).

Over the last decade there has been a big push by gambling regulators for gambling operators to be more socially responsible towards its clientele and this has led to the use of many different responsible gambling (RG) tools and initiatives such as voluntary self-exclusion schemes (where gamblers can ban themselves from gambling), limit setting (where gamblers can choose how much time and/or money they want to lose while gambling), personalized feedback (where gamblers can get personal feedback and advice based on their actual gambling behaviour) and pop-up messages (where gamblers receive a pop-up message during play that informs them how long they have been playing or how much money that have spent during the session).

However, very little is known about whether these RG tools and initiatives actually work, and most of the research that has been published relies on laboratory methods and self-reports – both of which have problems as reliable methods when it comes to evaluating whether RG tools work. Laboratory experiments typically contain very few participants and are carried out in non-ecologically valid settings, and self-reports are prone to many biases (including social desirability and recall biases). Additionally, the sample sizes are also relatively small (although bigger than experiments).

The datasets to analyse player behaviour are huge and can include hundreds of thousands of online gamblers. Given that my first empirical paper on gambling published in the Journal of Gambling Studies in 1990 was a participant observational analysis of eight slot machine gamblers at one British amusement arcade, it is extraordinary to think that decades later I have access to datasets beyond anything I could have imagined back in the 1980s when I began my research career. The data analysis is carried with my research colleague Michael Auer who has a specific expertise in data mining and we use traditional statistical tests to analyse the data. However, the hardest part is always trying to work out which parameters to use in assessing whether the RG tool worked or not. The kind of data we have includes how much time and money that players are spending on the gambling website, and using that data we can assess to what extent the amount of time and money decreases as a result of using limit setting measures, or receiving personalized feedback or a pop-up message.

One of the biggest problems in doing this type of research in the gambling studies field is getting access to the data in the first place and the associated issue of whether academics should be working with the gambling industry in the first place. The bottom line is that we would never have been able to undertake this kind of innovative research with participant sizes of hundreds of thousands of real gamblers without working in co-operation with the gambling industry. (It should also be noted that the gambling companies in question did not fund the research but provided simply provided access to their databases and customers). In fact, I would go as far as to say the research would have been impossible without gambling industry co-operation. Data access provided by the gambling industry has to be one of the key ways forward if the field is to progress.

Unlike other consumptive and potentially addictive behaviours (smoking cigarettes, drinking alcohol, etc.), researchers can study real-time gambling (and other potentially addictive behaviours like video gaming and social networking) in a way that just cannot be done in other chemical and behavioural addictions (e.g., sex, exercise, work, etc.) because of online and/or card-based technologies (such as loyalty cards and player cards). There is no equivalent of this is the tobacco or alcohol industry, and is one of the reasons why researchers in the gambling field are beginning to liaise and/or collaborate with gambling operators. As researchers, we should always strive to improve our theories and models and it appears strange to neglect this purely objective information simply because it involves working together with the gambling industry. This is especially important given the recent research by Dr. Julia Braverman and colleagues published in the journal Psychological Assessment using data from gamblers on the bwin website showing that self-recollected information does not match with objective behavioural tracking data.

The great thing about online behavioural tracking data collected from gamblers is that it is totally objective (as it provides a true record of what every gambler does click-by-click), is collected from real world gambling websites (so is ecologically valid), and has large sample sizes (typically tens of thousands of online gamblers). There of course some disadvantages, the main ones being that the sample is unrepresentative of all online gamblers (as the data only comes from gamblers at one website) and nothing is known about the person’s gambling activity at other websites (research has shown that online gamblers typically gamble at a number of different websites and not just one). Despite these limitations, the analysis of behavioural tracking data (so-called ‘big data’) is a reliable and cutting-edge way to assess and evaluate online gambling behaviour and to assess whether RG tools actually work in real world gambling settings with real online gamblers in real time.

To get access to such data you have to cultivate a trusting relationship with the data providers. It took me years to build up trust with the gambling industry because researchers who study problem gambling are often perceived by the gambling industry to be ‘anti-gambling’ but in my case this wasn’t true. I am ‘pro-responsible gambling’ and gamble myself so it would be hypocritical to be anti-gambling. My main aim in my gambling research is to protect players and minimise harm. Problem gambling will never be totally eliminated but it can be minimised. If gambling companies share the same aim and philosophy of not wanting to make money from problem gamblers but to make money from non-problem gamblers, then I would be prepared to help and collaborate.

You also need to be thick-skinned. If you are analysing any behavioural tracking data provided by the gambling industry, then you need to be prepared for others in the field criticizing you for working in collaboration with the industry. Although none of this research is funded by the industry, the fact that you are collaborating is enough for some people to accuse you of not being independent and/or being in the pockets of the gambling industry. Neither of these are true but it won’t stop the criticism. Nor will it stop me from carrying on researching in this area using datasets provided by the gambling industry.

Dr. Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Auer, M. & Griffiths, M.D. (2013). Behavioral tracking tools, regulation and corporate social responsibility in online gambling. Gaming Law Review and Economics, 17, 579-583.

Auer, M. & Griffiths, M.D. (2013). Voluntary limit setting and player choice in most intense online gamblers: An empirical study of gambling behaviour. Journal of Gambling Studies, 29, 647-660.

Auer, M. & Griffiths, M.D. (2014). Personalised feedback in the promotion of responsible gambling: A brief overview. Responsible Gambling Review, 1, 27-36.

Auer, M. & Griffiths, M.D. (2014). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, 30, 879-887.

Auer, M. & Griffiths, M.D. (2015). Testing normative and self-appraisal feedback in an online slot-machine pop-up message in a real-world setting. Frontiers in Psychology, 6, 339. doi: 10.3389/fpsyg.2015.00339.

Auer, M. & Griffiths, M.D. (2015). Theoretical loss and gambling intensity (revisited): A response to Braverman et al (2013). Journal of Gambling Studies, 31, 921-931.

Auer, M. & Griffiths, M.D. (2015). The use of personalized behavioral feedback for problematic online gamblers: An empirical study. Frontiers in Psychology, 6, 1406. doi: 10.3389/fpsyg.2015.01406.

Auer, M., Littler, A. & Griffiths, M.D. (2015). Legal aspects of responsible gaming pre-commitment and personal feedback initiatives. Gaming Law Review and Economics, 6, 444-456.

Auer, M., Malischnig, D. & Griffiths, M.D. (2014). Is ‘pop-up’ messaging in online slot machine gambling effective? An empirical research note. Journal of Gambling Issues, 29, 1-10.

Auer, M., Schneeberger, A. & Griffiths, M.D. (2012). Theoretical loss and gambling intensity: A simulation study. Gaming Law Review and Economics, 16, 269-273.

Braverman, J., Tom, M., & Shaffer, H. J. (2014). Accuracy of self-reported versus actual online gambling wins and losses. Psychological Assessment, 26, 865-877.

Griffiths, M.D. (1990). Addiction to fruit machines: A preliminary study among males. Journal of Gambling Studies, 6, 113-126.

Griffiths, M.D. & Auer, M. (2011). Approaches to understanding online versus offline gaming impacts. Casino and Gaming International, 7(3), 45-48.

Griffiths, M.D. & Auer, M. (2015). Research funding in gambling studies: Some further observations. International Gambling Studies, 15, 15-19.

Track to the future: Online behavioural tracking and problem gambling

Almost everyone reading this will be aware that problem gambling lies towards one end of a continuum that ranges from non-gambling at one end through to pathological gambling at the other. However, it should also be noted that there will always be some behaviours that are typically engaged in by problem gamblers that some non-problem gamblers may also engage in at least occasionally (e.g., chasing behaviour when gamblers try to recoup their losses).

Worldwide, there are many different screening instruments that can be used by clinicians and researchers to help identify problem gambling. One of most regularly used is the Diagnostic and Statistical Manual, Fourth Edition (of which the fifth edition has just been published) that includes criteria that can aid the diagnosis of problem and pathological gambling (but now called disordered gambling in its latest incarnation). The previous (DSM-IV) criteria were used in the most recent British Gambling Prevalence Survey published in 2011. If a person answered positively to at least five of the criteria, a diagnosis of pathological gambling would be made whereas endorsement of three or four of the criteria would indicate a diagnosis of problem gambling. Using the DSM-IV, the latest BGPS reported a problem gambling rate of 0.9% among British adults.

In contrast to offline gambling, the use of online behavioural tracking presents an opportunity for researchers to examine the actual and real-time behaviour engaged in by gamblers. Analysis of behavioural tracking data has been carried out by various groups of researchers. For instance, one group affiliated to Harvard University have published a series of papers examining a data set of online gamblers provided by the bwin gaming company. My own research unit has also been publishing data using behavioural tracking data provided by the win2day gaming company.

During my consultancy for various online gaming companies, I have been informed by industry insiders that problem gambling can be identified online by examining the patterns and behaviours of online gamblers. If this is true, it has implications for current problem gambling screens (including the new DSM-V). This is because most criteria found in these screens are associated with the consequences of problem gambling rather than the gambling behaviour itself. Take the DSM-IV. I have argued that only a few of the behaviours in the DSM criteria for pathological gambling can be reliably spotted online using online behavioural tracking (the most obvious being chasing losses, salience/preoccupation, and tolerance). The following list highlights each of the DSM-IV questions for pathological gambling and the component of pathological gambling that each criterion is assessing. This is followed by an assessment as to what extent each criterion can be identified online.

  • Salience/Preoccupation (Do you find that you are becoming preoccupied with past gambling successes or find yourself spending increasingly more time planning future gambling?) – An online problem gambler is likely to spend a lot of time gambling online although this behaviour in itself does not necessarily indicate a problem. Anything above four hours daily play over a protracted period could be considered excessive although some forms of online gambling (e.g., online poker) may take up a lot of time and be played relatively inexpensively.
  • Tolerance (Do you find that you need to increase the amount of money you gamble to achieve the same enjoyment and excitement?) – If experiencing tolerance to gambling, an online problem gambler is likely to have changed their gambling behaviour in one of two ways over time. The first example of tolerance is a gradual increase of daily play in terms of time. For instance, the gambler might start off playing 30-60 minutes a day but over the course of a few months starts to play increasing amounts of time. The second example of tolerance is the act of gambling using gradually bigger stakes over time. An online problem gambler is more likely to experience both of these combined (i.e., gambling for longer and longer periods of time with bigger and bigger amounts of money).
  • Relapse (Have you recently tried to stop gambling but were unsuccessful?) – Although this is difficult to detect with absolute certainty online, a typical pattern would be a gambler who gambles heavily, day-in day-out, for a period of time and then “disappears” for a period of time (which could be days, weeks, and sometimes even months), only to suddenly re-appear and gamble heavily again.
  • Withdrawal  (Do you become moody or impatient when you are cutting down how much you gamble?) This is again difficult to detect with absolute certainty online but is most likely to surface with the use of verbally aggressive comments in those games that have chat room facilities (such as online poker).
  • Escape from reality (Do you ever use gambling a way of ignoring stress in your in life or even pick you up when you feel down?) – This is almost impossible to detect online although those players who play for long hours every day are more likely to experience escape-like feeling.
  • Chasing losses (Do you ever try to win back the money you lost by increasing the size or frequency of your wagers?) – This is one of the key indicators of problem gambling and can be spotted online more easily than many other problem gambling criteria. Typical chasing patterns will include repeated ‘double or quit’ strategies in an effort to recoup losses. Although many gamblers use this strategy on occasion, the online problem gambler will do it repeatedly. This behaviour, above and beyond any other criteria, is most likely to signal problem gambling.
  • Conceal Involvement (Do you ever hide how much or how often you gamble from significant others?) – There is no way that an online gambling operator can spot this during online gambling unless such admissions are given to other players in online chat rooms.
  • Unsociable Behaviour (Have you ever committed fraud or theft to get money to gamble with?) – Again, there is no way that an online gambling operator can spot this during online gambling unless such admissions are given to other players in online chat rooms.
  • Ruin a Relationship/Opportunity (Has gambling ever ruined a personal relationship or an occupational or educational opportunity?) – As with the previous two criteria, there is no way that an online gambling operator can spot this during online gambling unless such admissions are given to other players in online chat rooms.
  • Bail-out  (Have you ever needed others to relieve a financial problem created by gambling?) – When an online gambler has exhausted all their own funds, they will often ‘beg, borrow and (eventually) steal’ money to continue gambling. A player whose account is constantly ‘topped up’ by people other than themselves may be a problem gambler.

This brief analysis of the extent to which each DSM criterion of problem gambling can be identified online shows that only a few behaviours can be reliably spotted via online behavioural tracking. The following list contains a number of behaviours that are engaged in by online problem gamblers. This was devised and based on my conversations with members of online gaming industry. These are additional to those identified above (i.e., chasing losses, spending high amounts of time and money, and increasing the amount of gambling over time). As a general ‘rule of thumb’, it is assumed that the more of these online behaviours that are engaged in by an individual, the more likely that person is to be a problem gambler.

  • Playing a variety of stakes – Playing a variety of different stakes (in games like online poker) indicates poor planning and may be a cue or precursor to chasing behaviour.
  • Playing a variety of games – Evidence from national prevalence surveys (e.g. Wardle et, al, 2011) demonstrates that the more types of gambling engaged in, the more likely the person is to be a problem gambler. Although this factor on its own is unlikely to indicate problem gambling, when combined with other indicators on this list may be indicative of problem gambling.
  • Player ‘reload’ within gambling session – Although any gambler can engage in such behaviour, players who deposit more money within session (‘reload’) are more likely to be problem gamblers. This indicates poor planning and is a cue to chasing behaviour.
  • Frequent payment method changes – The constant changing of deposit payment methods indicates poor planning and is may be a cue to chasing behaviour. This online behaviour usually indicates shortage of funds and need to extract monies from a variety of sources. Such behaviour can also indicate bank refusal.
  • Verbal aggression – Aggressive verbal interaction via relay chat is common among problem gamblers although any gambler losing money may cause such behaviour. Such behaviour may be evidence of gamblers going on ‘tilt’ (i.e., negative cognitive and emotional reaction to losing) or withdrawal effects if out of money to gamble.
  • Constant complaints to customer services – Constant complaints to the customer service department is common among problem gamblers although any gambler losing money may cause such behaviour. As with verbal aggression, such behaviour may be evidence of gamblers going on ‘tilt’ (i.e., negative cognitive and emotional reaction to losing).

Clearly, each of these behaviours needs to be examined in relation to at least three or four other indicative behaviours. Perhaps most importantly, and according to online gambling companies who use socially responsible behavioural tracking tools, it is a significant change in usual online behaviour that is most indicative of a problem gambler. Most statistical modelling of player behaviour predicts future problematic behaviour on the basis of behavioural change over time. The behaviours highlighted suggest that screening instruments in the future may be able to be developed that concentrate on the gambling behaviour itself, rather than the associated negative consequences.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Auer, M. & Griffiths, M.D. (2013). Limit setting and player choice in most intense online gamblers: An empirical study of online gambling behaviour. Journal of Gambling Studies, in press.

Auer, M. & Griffiths, M.D. (2013). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, in press.

Delfabbro, P.H., King, D.L & Griffiths, M.D. (2012). Behavioural profiling of problem gamblers: A critical review. International Gambling Studies, 12, 349-366.

Dragicevic, S., Tsogas, G., & Kudic, A. (2011). Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection. International Gambling Studies, 11, 377–391.

Griffiths, M.D. (2009). Social responsibility in gambling: The implications of real-time behavioural tracking. Casino and Gaming International, 5(3), 99-104.

Griffiths, M.D. & Auer, M. (2011). Approaches to understanding online versus offline gaming impacts. Casino and Gaming International, 7(3), 45-48.

Griffiths, M.D. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.

LaBrie, R.A., Kaplan, S., LaPlante, D.A., Nelson, S.E., & Shaffer, H.J. (2008). Inside the virtual casino: A prospective longitudinal study of Internet casino gambling. European Journal of Public Health, DOI:10.1093/eurpub/ckn021.

LaPlante, D.A., Kleschinsky, J.H., LaBrie, R.A., Nelson, S.E. & Shaffer, H.J. (2009). Sitting at the virtual poker table: A prospective epidemiological study of actual Internet poker gambling behavior. Computers in Human Behavior 25, 711-717.

Wardle, H., Moody, A., Spence, S., Orford, J., Volberg, R., Jotangia, D., Griffiths, M., Hussey, D. & Dobbie, F. (2011). British Gambling Prevalence Survey 2010. London: The Stationery Office.