Brent J. Hecht

Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage

While applications for mobile devices have become extremely important in the last few years, little public information exists on mobile application usage behavior. We describe a large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices. We present two types of results from analyzing this data: basic descriptive statistics and contextual descriptive statistics. In the case of the former, we find that the average session with an application lasts less than a minute, even though users spend almost an hour a day using their phones. Our contextual findings include those related to time of day and location. For instance, we show that news applications are most popular in the morning and games are at night, but communication applications dominate through most of the day. We also find that despite the variety of apps available, communication applications are almost always the first used upon a devices waking from sleep. In addition, we discuss the notion of a virtual application sensor, which we used to collect the data.

Omnipedia: bridging the wikipedia language gap

We present Omnipedia, a system that allows Wikipedia readers to gain insight from up to 25 language editions of Wikipedia simultaneously. Omnipedia highlights the similarities and differences that exist among Wikipedia language editions, and makes salient information that is unique to each language as well as that which is shared more widely. We detail solutions to numerous front-end and algorithmic challenges inherent to providing users with a multilingual Wikipedia experience. These include visualizing content in a language-neutral way and aligning data in the face of diverse information organization strategies. We present a study of Omnipedia that characterizes how people interact with information using a multilingual lens. We found that users actively sought information exclusive to unfamiliar language editions and strategically compared how language editions defined concepts. Finally, we briefly discuss how Omnipedia generalizes to other domains facing language barriers.

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.

Explanatory semantic relatedness and explicit spatialization for exploratory search

Exploratory search, in which a user investigates complex concepts, is cumbersome with todays search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram -- from a periodic table, to a parliamentary seating chart, to a world map -- as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedias text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.

Geographic human-computer interaction

Researchers and practitioners in human-computer interaction are increasingly taking geographic approaches to their work. Whether designing novel location-based systems, developing natural user interfaces for maps, or exploring online interactions over space and time, HCI is discovering that geographic questions, methods, and use cases are becoming integral to our field. Unfortunately, to our knowledge, there have been no direct efforts to unite members of the community exploring geographic HCI. The goal of this forum is to bring together researchers from a variety of areas to provide a summary of what has been done thus far and to discuss options for developing a more formal geographic HCI community. We will also highlight the troublesome lack of communication between scholars in geography and HCI and the opportunities that will result from increased collaboration between the two fields.

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.

The Success and Failure of Quality Improvement Projects in Peer Production Communities

Peer production communities have been proven to be successful at creating valuable artefacts, with Wikipedia as a prime example. However, a number of studies have shown that work in these communities tends to be of uneven quality and certain content areas receive more attention than others. In this paper, we examine the efficacy of a range of targeted strategies to increase the quality of under-attended content areas in peer production communities. Mining data from five quality improvement projects in the English Wikipedia, the largest peer production community in the world, we show that certain types of strategies (e.g. creating artefacts from scratch) have better quality outcomes than others (e.g. improving existing artefacts), even if both are done by a similar cohort of participants. We discuss the implications of our findings for Wikipedia as well as other peer production communities.

SubwayPS: towards smartphone positioning in underground public transportation systems

Thanks to rapid advances in technologies like GPS and Wi-Fi positioning, smartphone users are able to determine their location almost everywhere they go. This is not true, however, of people who are traveling in underground public transportation networks, one of the few types of high-traffic areas where smartphones do not have access to accurate position information. In this paper, we introduce the problem of underground transport positioning on smartphones and present SubwayPS, an accelerometer-based positioning technique that allows smartphones to determine their location substantially better than baseline approaches, even deep beneath city streets. We highlight several immediate applications of positioning in subway networks in domains ranging from mobile advertising to mobile maps and present MetroNavigator, a proof-of-concept smartphone and smartwatch app that notifies users of upcoming points-of-interest and alerts them when it is time to get ready to exit the train.

The Effect of Population and "Structural" Biases on Social Media-based Algorithms: A Case Study in Geolocation Inference Across the Urban-Rural Spectrum

Much research has shown that social media platforms have substantial population biases. However, very little is known about how these population biases affect the many algorithms that rely on social media data. Focusing on the case study of geolocation inference algorithms and their performance across the urban-rural spectrum, we establish that these algorithms exhibit significantly worse performance for underrepresented populations (i.e. rural users). We further establish that this finding is robust across both text- and network-based algorithm designs. However, we also show that some of this bias can be attributed to the design of algorithms themselves rather than population biases in the underlying data sources. For instance, in some cases, algorithms perform badly for rural users even when we substantially overcorrect for population biases by training exclusively on rural data. We discuss the implications of our findings for the design and study of social media-based algorithms.

Informing online and mobile map design with the collective wisdom of cartographers

Despite the large and growing prominence of online and mobile maps, they have not been broadly and systematically examined with a lens informed by traditional cartography. Using an approach rooted in cartographic theory and a unique dataset of 382 publicly-displayed local maps, we identify the collective wisdom of hundreds of cartographers with respect to a number of cartographic design decisions. We compare our findings to the approaches taken in popular online and mobile map platforms and develop suggestions for incorporating the collective wisdom of cartographers into these systems. Our suggestions include the adoption of location-aware cartography, in which cartographic approaches are intelligently varied based on the type of location being viewed. We provide mockup designs of online and mobile maps that implement our suggestions and discuss means by which the surprising gap between online and mobile maps and traditional cartography may be bridged.