References
- AdamiN., BeniniS., BoschettiA., CaniniL., MaioneF., & TemporinM. (2013). Markers of unsustainable gambling for early detection of at-risk online gamblers. International Gambling Studies, 13, 188–204.
- AllcockC. (2002). Current issues related to identifying the problem gambler in the gaming venue. Australian Gaming Council: Current issues. Melbourne: Australian Gaming Council.
- AlpersG. W., WinzelbergA. J., ClassenC., RobertsH., DevP., KoopmanC., & TaylorC. B. (2005). Evaluation of computerized text analysis in an Internet breast cancer support group. Computers in Human Behavior, 21, 361–376.
- American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders(5th ed.). Washington, DC: Author.
- AuerM., & GriffithsM. (2013). Behavioral tracking tools, regulation, and corporate social responsibility in online gambling. Gaming Law Review and Economics, 17, 579–583.
- AuerM., & GriffithsM. (2014). Personalised feedback in the promotion of responsible gambling: A brief overview. Responsible Gambling Review, 1, 27–36.
- BernhardB. J., LucasA. F., & JangD. (2006). Responsible Gaming Device Research Report. Las Vegas: University of Nevada.
- BlaszczynskiA., & NowerL. (2002). A pathways model of problem and pathological gambling. Addiction, 97, 487–499.
- BravermanJ., LaPlanteD. A., NelsonS. E., & ShafferH. J. (2013). Using cross-game behavioral markers for early identification of high-risk internet gamblers. Psychology of Addictive Behaviors, 27, 868–877.
- BravermanJ., & ShafferH. J. (2012). How do gamblers start gambling: Identifying behavioral markers for high-risk Internet gambling. European Journal of Public Health, 22, 273–278.
- CantinottiM., & LadouceurR. (2008). Harm Reduction and Electronic Gambling Machines: Does this Pair Make a Happy Couple or is Divorce Foreseen?Journal of Gambling Studies, 24, 39–54.
- CoussementK., & Van den PoelD. (2008). Improving customer complaint management by automatic email classification using linguistic style features as predictors. Decision Support Systems, 44, 870–882.
- DelfabbroP., BorgasM., & KingD. (2012). Venue staff knowledge of their patrons' gambling and problem gambling. Journal of Gambling Studies, 28, 155–169.
- DelfabbroP., KingD., & GriffithsM. (2012). Behavioural profiling of problem gamblers: A summary and review. International Gambling Studies, 12, 349–366.
- DelfabbroP., OsbornA., NevileM., SkeltL., & McMillenJ. (2007). Identifying problem gamblers in gambling venues. Melbourne: Gambling Research Australia.
- DragicevicS., PercyC., KudicA., & ParkeJ. (2013). A descriptive analysis of demographic and behavioral data from internet gamblers and those who self-exclude from online gambling platforms. Journal of Gambling Studies. Advance Access.10.1007/s10899-013-9418-1.
- GainsburyS. (2011). Player account-based gambling: Potentials for behaviour-based research methodologies. International Gambling Studies, 11, 153–171.
- Grall-BronnecM., WainsteinL., FeuilletF., BoujuG., RocherB., VenisseJ. L., & Sebille-RivainV. (2012). Clinical profiles as a function of level and type of impulsivity in a sample group of at-risk and pathological gamblers seeking treatment. Journal of Gambling Studies, 28, 239–252.
- GravesK. D., SchmidtJ. E., BollmerJ., FejarM., LangerS., BlonderL. X., & AndrykowskiM. A. (2005). Emotional expression and emotional recognition in breast cancer survivors: A controlled comparison. Psychology & Health, 20, 579–595.
- GrayH. M., LaPlanteD. A., & ShafferH. J. (2012). Behavioral characteristics of internet gamblers who trigger corporate responsible gambling interventions. Psychology of Addictive Behaviors, 26, 527–535.
- GriffithsM. (2011). Empirical internet gambling research (1996–2008): Some further comments. Addiction Research & Theory, 19, 85–86.
- GriffithsM., & WhittyM. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104–117.
- GrimmerJ., & StewartB. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21, 267–297.
- HäfeliJ., & LischerS. (2010). Die Früherkennung von Problemspielern in Schweizer Kasinos. Eine repräsentative, quantitative Datenanalyse der ReGaTo-Daten 2006 [early detection of problem gamblers in Swiss casinos. A representative quantitative analysis of the ReGaTo data 2006]. Prävention und Gesundheitsförderung, 5, 145–150.
- HäfeliJ., LischerS., & SchwarzJ. (2011). Early detection items and responsible gambling features for online gambling. International Gambling Studies, 11, 273–288.
- HäfeliJ., & SchneiderC. (2005). Identifikation von Problemspielern im Kasino – ein Screeninginstrument (ID-PS) [Identification of problem gamblers in casinos – a screening tool]. Luzern: Hochschule Luzern – Soziale Arbeit.
- KahnJ. H., TobinR. M., MasseyA. E., & AndersonJ. A. (2007). Measuring emotional expression with the linguistic inquiry and word count. American Journal of Psychology, 120, 263–286.
- LaBrieR. A., KaplanS. A., LaPlanteD. A., NelsonS. E., & ShafferH. J. (2008). Inside the virtual casino: A prospective longitudinal study of actual Internet casino gambling. European Journal of Public Health, 18, 410–416.
- LaBrieR. A., LaPlanteD. A., NelsonS. E., SchumannA., & ShafferH. J. (2007). Assessing the playing field: A prospective longitudinal study of internet sports gambling behavior. Journal of Gambling Studies, 23, 347–362.
- LaBrieR. A., & ShafferH. J. (2011). Identifying behavioral markers of disordered Internet sports gambling. Addiction Research & Theory, 19, 56–65.
- LadouceurR., JacquesC., GirouxI., FerlandF., & LeblondJ. (2000). Analysis of a casino's selfexclusion program. Journal of Gambling Studies, 16, 453–460.
- LaPlanteD. A., NelsonS. E., & GrayH. M. (2014). Breadth and depth involvement: Understanding internet gambling involvement and its relationship to gambling problems. Psychology of Addictive Behaviors, 28, 396–403.
- LaPlanteD. A., KleschinskyJ. H., LaBrieR. A., NelsonS. E., & ShafferH. 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.
- MatthewsR. (1996). Base-rate errors and rain forecasts. Nature, 382, 766–766.
- MeyerG., & HayerT. (2008). Die Identifikation von Problemspielern in Spielstätten [Identification of problem gamblers in venues]. Prävention und Gesundheitsförderung, 2, 67–74.
- MonaghanS., & BlaszczynskiA. (2010). Impact of mode of display and message content of responsible gambling signs for electronic gaming machines on regular gamblers. Journal of Gambling Studies, 26, 67–88.
- NashV., O'ConnellR., ZevenbergenB., & MishkinA. (2014). Effective age verification techniques: Lessons to be learnt from the online gambling industry (Final Report). Oxford: Oxford Internet Institute.
- NeumanY., CohenY., AssafD., & KedmaG. (2012). Proactive screening for depression through metaphorical and automatic text analysis. Artificial Intelligence in Medicine, 56, 19–25.
- NowatzkiN. R., & WilliamsR. J. (2002). Casino self-exclusion programmes: A review of the issues. International Gambling Studies, 2, 3–25.
- ParkeJ., RigbyeJ., & ParkeA. (2008). Cashless and card-based technologies in gambling: A review of the literature. Report commissioned by the UK Gambling Commission. Salford: University of Salford.
- PennebakerJ. W., ChungC. K., IrelandM., GonzalesA., & BoothR. J. (2007). The development and psychometric properties of LIWC. [Software manual].
- PestianJ. P., MatykiewiczP., Linn-GustM., SouthB., UzunerO., WiebeJ., … BrewCh. (2012). sentiment analysis of suicide notes: A shared task. Biomedical Informatics Insights, 5, 3–16.
- RudeS., GortnerE. M., & PennebakerJ. (2004). Language use of depressed and depression-vulnerable college students. Cognition & Emotion, 18, 1121–1133.
- SchellinckT., & SchransT. (2004). Identifying problem gamblers at the gambling venue: Finding combinations of high confidence indicators. Gambling Research: Journal of the National Association for Gambling Studies (Australia), 16, 8–24.
- TumasjanA., SprengerT. O., SandnerP. G., & WelpeI. M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of the Fourth AAAI Conference on Weblogs and Social Media. Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
- WardleH., MoodyA., SpenceS., OrfordJ., VolbergR., JotangiaD., … DobbieF. (2011). British Gambling Prevalence Survey 2010. Report prepared for the Gambling Commission. London: National Centre for Social Research.
- WolfM., HornA., MehlM., HaugS., PennebakerJ. W., & KordyH. (2008). Computergestützte quantitative Textanalyse: Äquivalenz und Robustheit der deutschen Version des Linguistic Inquiry and Word Count [Computer-aided quantitative text analysis: Equivalence and robustness of the German adaption of the Linguistic Inquiry and Word Count]. Diagnostica, 54, 85–98.
- YuB., KaufmannS., & DiermeierD. (2008). Exploring the characteristics of opinion expressions for political opinion classification. In Proceedings of the 2008 international conference on Digital government research. Marina Del Rey: Digital Government Society.