In this work we present a mobile application we designed and engineered to enable people to log their travels near and far, leave notes behind, and build a community around spaces in between destinations. Our design explores new ground for location-based social computing systems, identifying opportunities where these systems can foster the growth of on-line communities rooted at non-places. In our work we develop, explore, and evaluate several innovative features designed around four usage scenarios: daily commuting, long-distance traveling, quantified traveling, and journaling. We present the results of two small-scale user studies, and one large-scale, world-wide deployment, synthesizing the results as potential opportunities and lessons learned in designing social computing for non-places.
Check-in apps emerged around 2003 to enable a virtual social experience anchored around physical places @cite_34 . These apps allow users to broadcast their presence at venues, that is, the places they go to. Venues include restaurants, bars, offices, apartment buildings, homes, museums, parks, movie theatres, shops, and caf ' e s. Dodgeball, GoWalla, Foursquare and now Swarm are some of the apps that embodied this concept of checking-in to a venue. More recently, Facebook has adopted this as a feature that people can use to attach a location to their posts.
@cite_34
109765122 1977705987 2013581884 2294749418
In August 2010 Facebook launched Places, a location-based service that allows users to check into points of interest and share their physical whereabouts with friends. The friends who see these events in their News Feed can then respond to these check-ins by liking or commenting on them. These data consisting of the places people go and how their friends react to them are a rich, novel dataset. In this paper we first analyze this dataset to understand the factors that influence where users check in, including previous check-ins, similarity to other places, where their friends check in, time of day, and demographics. We show how these factors can be used to build a predictive model of where users will check in next. Then we analyze how users respond to their friends’ check-ins and which factors contribute to users liking or commenting on them. We show how this can be used to improve the ranking of check-in stories, ensuring that users see only the most relevant updates from their friends and ensuring that businesses derive maximum value from check-ins at their establishments. Finally, we construct a model to predict friendship based on check-in count and show that cocheck-ins has a statistically significant effect on friendship. Location-based social networking platform (e.g., Foursquare), as a popular scenario of participatory sensing system that collects heterogeneous information (such as tips and photos) of venues from users, has attracted much attention recently. In this paper, we study the distribution of these information and their relationship, based on a large dataset crawled from Foursquare, which consists of 2,728,411 photos, 1,212,136 tips and 148,924,749 check-ins of 190,649 venues, contributed by 508,467 users. We analyze the distribution of user-generated check-ins, venue photos and venue tips, and show interesting category patterns and correlation among these information. In addition, we make the following observations: i) Venue photos in Foursquare are able to significantly make venues more social and popular. ii) Users share venue photos highly related to food category. iii) Category dynamics of venue photo sharing have similar patterns as that of venue tips and user check-ins at the venues. iv) Users tend to share photos rather than tips. We distribute our data and source codes under the request of research purposes (email: yi.yu.yy@gmail.com). Discovering groups of online friends who go to the same physical places has numerous potential applications including privacy management, friend recommendation, and contact grouping as in Google+ circles. Until recently, little information was available about places visited by users of online social networking services, so community detection on the social graph could not take this into account. With the rise of services such as Foursquare, Gowalla, and Facebook Places, where users check in to named venues and share their location with their friends, we now have the right data to make this possible. In this work, we propose a way to extract place-focused communities from the social graph by annotating its edges with check-in information. Using traces from two online social networks with location sharing, we show that we can extract groups of friends who meet face-to-face, with many possible benefits for online social services. Location sharing services (LSS) like Foursquare, Gowalla, and Facebook Places support hundreds of millions of user-driven footprints (i.e., "checkins"). Those global-scale footprints provide a unique opportunity to study the social and temporal characteristics of how people use these services and to model patterns of human mobility, which are significant factors for the design of future mobile+location-based services, traffic forecasting, urban planning, as well as epidemiological models of disease spread. In this paper, we investigate 22 million checkins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. We find that: (i) LSS users follow the “Levy Flight” mobility pattern and adopt periodic behaviors; (ii) While geographic and economic constraints affect mobility patterns, so does individual social status; and (iii) Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services.