Gamblers' habit

The theories used to understand and predict regular non-problem gambling are almost exclusively affective or cognitive-oriented. These include motives, self-esteem, image enhancement and illusions of control over random events. However, gambling is one of the most frequently purchased consumer products, and the frequency of past behavior has traditionally been viewed as “habit” by psychologists and marketers. While habit as the frequency of past behavior has been shown to be a strong predictor of future behavior in gambling, habit offers little additional insight into gambling behavior in that form.

The frequency of past purchasing behavior is an important input to NBD-Dirichlet models that provide an enhanced ability to understand and predict future purchases of frequently purchased consumer package goods. NBD-Dirichlet models have been shown to provide an excellent fit to data for a broad range of frequently purchased goods and services for countries across the world. Applications of the NBD-Dirichlet models to data concerning gambling behavior show that these models consistently provide an even closer fit to the data than with other consumer models tested.

The interpretation of NBD-Dirichlet output can provide more accurate benchmarks than cognitive or affective output to test changes to the gambling environment (e.g., more games, new games, warnings) and to gamblers (e.g., problem gambling). The implications and use of the NBD-Dirichlet statistics for gambling providers and public policy is discussed.

Machine learning approaches for modeling spammer behavior

Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Naïve Bayesian classifier (Naive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.

A modeling intervention in heart failure

BACKGROUND: Peak oxygen uptake (VO(2)) testing is commonly used to assess chronic heart failure (CHF) patients' exercise tolerance. The test requires maximal effort; however, many participants have low confidence (self-efficacy) to perform optimally. PURPOSE: This randomized controlled trial examined the effectiveness of a modeling intervention to increase Peak VO(2) (PVO(2)) and self-efficacy in people diagnosed with CHF. METHODS: Twenty participants with a diagnosis of CHF were randomized to either an intervention (modeling DVD) or a control group. Both groups completed a measure of self-efficacy prior to performing two PVO(2) tests, each separated by 7 days. After completing the first test (T1) the intervention group watched a 10-min coping model DVD. All participants returned 1 week later (T2) to complete identical study procedures. RESULTS: Analysis of covariance results showed that compared with the participants in the control group, those assigned to the modeling intervention had higher PVO(2) at T2, F (1, 19) = 4.38, p = 0.05, eta (2) = 0.21 and self-efficacy, F (1, 19) = 5.80, p < 0.05, eta (2) = 0.25. Only partial support was found for change in self-efficacy mediating treatment outcome (PVO(2)). CONCLUSIONS: Watching a modeling video is associated with increased PVO(2) and self-efficacy. These results have implications for testing patients in a clinical setting to maximize exercise tolerance test results.