Jacob Thebault-Spieker

The Geography of Pokémon GO: Beneficial and Problematic Effects on Places and Movement

The widespread popularity of Pokémon GO presents the first opportunity to observe the geographic effects of location-based gaming at scale. This paper reports the results of a mixed methods study of the geography of Pokémon GO that includes a five-country field survey of 375 Pokémon GO players and a large scale geostatistical analysis of game elements. Focusing on the key geographic themes of places and movement, we find that the design of Pokémon GO reinforces existing geographically-linked biases (e.g. the game advantages urban areas and neighborhoods with smaller minority populations), that Pokémon GO may have instigated a relatively rare large-scale shift in global human mobility patterns, and that Pokémon GO has geographically-linked safety risks, but not those typically emphasized by the media. Our results point to geographic design implications for future systems in this space such as a means through which the geographic biases present in Pokémon GO may be counteracted.

User Session Identification Based on Strong Regularities in Inter-activity Time

Session identification is a common strategy used to develop metrics for web analytics and perform behavioral analyses of user-facing systems. Past work has argued that session identification strategies based on an inactivity threshold is inherently arbitrary or has advocated that thresholds be set at about 30 minutes. In this work, we demonstrate a strong regularity in the temporal rhythms of user initiated events across several different domains of online activity (incl. video gaming, search, page views and volunteer contributions). We describe a methodology for identifying clusters of user activity and argue that the regularity with which these activity clusters appear implies a good rule-of-thumb inactivity threshold of about 1 hour. We conclude with implications that these temporal rhythms may have for system design based on our observations and theories of goal-directed human activity.

Toward a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit

Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography—distance decay, structured variation in population density, mental maps, and “the Big Sort” (spatial homophily)—manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.

Freedom versus Standardization: Structured Data Generation in a Peer Production Community

In addition to encyclopedia articles and software, peer production communities produce structured data, e.g., Wikidata and OpenStreetMaps metadata. Structured data from peer production communities has become increasingly important due to its use by computational applications, such as CartoCSS, MapBox, and Wikipedia infoboxes. However, this structured data is usable by applications only if it follows standards. We did an interview study focused on OpenStreetMaps knowledge production processes to investigate how -- and how successfully -- this community creates and applies its data standards. Our study revealed a fundamental tension between the need to produce structured data in a standardized way and OpenStreetMaps tradition of contributor freedom. We extracted six themes that manifested this tension and three overarching concepts, correctness, community, and code, which help make sense of and synthesize the themes. We also offered suggestions for improving OpenStreetMaps knowledge production processes, including new data models, sociotechnical tools, and community practices (e.g. stronger leadership).

Never Too Old, Cold or Dry to Watch the Sky: A Survival Analysis of Citizen Science Volunteerism

CoCoRaHS is a multinational citizen science project for observing precipitation. Like many citizen science projects, volunteer retention is a key measure of engagement and data quality. Through survival analysis, we found that participant age (self-reported at account creation) is a significant predictor of retention. Compared to all other age groups, participants aged 60-70 are much more likely to sign up for CoCoRaHS, and to remain active for several years. We also measured the influence of task difficulty and the relative frequency of rain, finding small but statistically significant and counterintuitive effects. Finally, we confirmed previous work showing that participation levels within the first month are highly predictive of eventual retention. We conclude with implications for observational citizen science projects and crowdsourcing research in general.

Simulation Experiments on (the Absence of) Ratings Bias in Reputation Systems

As the gig economy continues to grow and freelance work moves online, five-star reputation systems are becoming more and more common. At the same time, there are increasing accounts of race and gender bias in evaluations of gig workers, with negative impacts for those workers. We report on a series of four Mechanical Turk-based studies in which participants who rated simulated gig work did not show race- or gender bias, while manipulation checks showed they reliably distinguished between low- and high-quality work. Given prior research, this was a striking result. To explore further, we used a Bayesian approach to verify absence of ratings bias (as opposed to merely not detecting bias). This Bayesian test let us identify an upper- bound: if any bias did exist in our studies, it was below an average of 0.2 stars on a five-star scale. We discuss possible interpretations of our results and outline future work to better understand the results.