Complex systems, abstractly represented as networks, are ubiquitous in everyday life. Analyzing and understanding these systems requires, among others, tools for community detection. As no single best community detection algorithm can exist, robustness across a wide variety of problem settings is desirable. In this work, we present Synwalk, a random walk-based community detection method. Synwalk builds upon a solid theoretical basis and detects communities by synthesizing the random walk induced by the given network from a class of candidate random walks. We thoroughly validate the effectiveness of our approach on synthetic and empirical networks, respectively, and compare Synwalk’s performance with the performance of Infomap and Walktrap (also random walk-based), Louvain (based on modularity maximization) and stochastic block model inference. Our results indicate that Synwalk performs robustly on networks with varying mixing parameters and degree distributions. We outperform Infomap on networks with high mixing parameter, and Infomap and Walktrap on networks with many small communities and low average degree. Our work has a potential to inspire further development of community detection via synthesis of random walks and we provide concrete ideas for future research.

Monitoring hygiene and motivation factors from Herzberg’s Two-Factor Theory is a popular way of understanding the influential aspects for employee satisfaction and motivation. The increased availability of employee feedback comprised in online employer reviews yields a promising data source to learn more about these influential factors and the theory itself. However, the application of the Two-Factor Theory to such reviews is yet missing. Thus, this article demonstrates how to apply this theory to more than 2 million online employer reviews. For that, review aspects as well as review ratings are leveraged to study factors that influence employee satisfaction with respect to the theory. Based on the gained insights, a prediction experiment is conducted to forecast employee satisfaction through the textual content of reviews. The presented approach identifies relevant aspects for satisfied and dissatisfied employees working in 43 different industries and 4 different countries. Overall, the results indicate that hygiene factors are most relevant for reviewers, while motivation factors are rather incidental. The prediction experiment achieves a mean balanced accuracy of 0.87, suggesting that review text is predictive for employee satisfaction. Further, the article adds useful input to the discussion of the Two-Factor Theory by providing comparative results across a plethora of different employers, industries and countries.

Protecting gamblers from problematic gambling behavior is a major concern for clinicians, researchers, and gambling regulators. Most gambling operators offer a range of so-called responsible gambling tools to help players better understand and control their gambling behavior. One such tool is voluntary self-exclusion, which allows players to block themselves from gambling for a self-selected period. Using player tracking data from three online gambling platforms operating across six countries, this study empirically investigated the factors that led players to self-exclude. Specifically, the study tested (i) which behavioral features led to future self-exclusion, and (ii) whether monetary gambling intensity features (i.e., amount of stakes, losses, and deposits) additionally improved the prediction. A total of 25,720 online gamblers (13% female; mean age = 39.9 years) were analyzed, of whom 414 (1.61%) had a future self-exclusion. Results showed that higher odds of future self-exclusion across countries was associated with a (i) higher number of previous voluntary limit changes and self-exclusions, (ii) higher number of different payment methods for deposits, (iii) higher average number of deposits per session, and (iv) higher number of different types of games played. In five out of six countries, none of the monetary gambling intensity features appeared to affect the odds of future self-exclusion given the inclusion of the aforementioned behavioral variables. Finally, the study examined whether the identified behavioral variables could be used by machine learning algorithms to predict future self-exclusions and generalize to gambling populations of other countries and operators. Overall, machine learning algorithms were able to generalize to other countries in predicting future self-exclusions.

In order to protect gamblers, gambling operators have introduced a wide range of responsible gambling (RG) tools. Mandatory play breaks (i.e., forced termination of a gambling session) and personalized feedback about the gambling expenditure are two RG tools that are frequently used. While the motivation behind mandatory play breaks is simple (i.e., gambling operators expect gamblers to reduce their gambling significantly as a result of an enforced break in play), empirical evidence supporting the efficacy of the mandatory breaks is still limited. The present study comprised a real-world experiment with the clientele of Norwegian gambling operator Norsk Tipping. On the Norsk Tipping gambling website, which offers slots, bingo and sports-betting, forced termination occurs if gamblers have played continuously for a one-hour period. The study tested the effect of different lengths of mandatory play breaks (90 s, 5 min, 15 min) on subsequent gambling behavior, as well as the effect of combined personalized feedback concerning money wagered, won, and net win/loss. In total 21,129 online players (61% male; mean age = 47.4 years) experienced at least one play break between April 17 and May 21 (2020) with 156,989 mandatory play breaks in total. Results indicated that a 15-min mandatory play break led to a disproportionately longer voluntary play pause compared to 5-min and 90-s mandatory play breaks. Personalized feedback appeared to have no additional effect on subsequent gambling and none of the mandatory play breaks appeared to affect the increase or decrease in money wagered once players started to gamble again.