Location sharing services (LSS) like Foursquare, Gowalla, and Facebook Places support hundreds of millions of userdriven 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 “Lèvy 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. 1

Application markets have revolutionized the software download model of mobile phones: third-party application developers offer software on the market that users can effortlessly install on their phones. This great step forward, however, also imposes some threats to user privacy: applications often ask for permissions that reveal private information such as the user’s location, contacts and messages. While some mechanisms to prevent leaks of user privacy to applications have been proposed by the research community, these solutions fail to consider that application markets are primarily driven by advertisements that rely on accurately profiling the user. In this paper we take into account that there are two parties with conflicting interests: the user, interested in maintaining their privacy and the developer who would like to maximize their advertisement revenue through user profiling. We have conducted an extensive analysis of more than 250,000 applications in the Android market. Our results indicate that the current privacy protection mechanisms are not effective as developers and advert companies are not deterred. Therefore, we designed and implemented a market-aware privacy protection framework that aims to achieve an equilibrium between the developer’s revenue and the user’s privacy. The proposed framework is based on the establishment of a feedback control loop that adjusts the level of privacy protection on mobile phones, in response to advertisement generated revenue. 1.

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The current social media landscape has resulted in a situation where people are encouraged to share a greater amount of information about their day-to-day lives than ever before. In this environment a large amount of personal data is disclosed in a public forum with little to no regard for the potential privacy impacts. This paper focuses on the presence of geographic data within images, metadata and individual postings. The GeoIntelligence project aims to aggregate this information to educate users on the possible implications of the utilisation of these services as well as providing service to law enforcement and business. This paper demonstrates the ability to profile users on an individual and group basis from data posted openly to social networking services.

The Microsoft Kinect sensor can be combined with a modern mobile phone to rapidly create digitally augmented environments. This can be used either directly as a form of ubiquitous computing environment or indirectly as framework for rapidly prototyping ubicomp environments that are otherwise implemented using conventional sensors. We describe an Android mobile application that supports rapid prototyping of spacial interaction by using 3D position data from the Kinect to simulate a proximity sensor. This allows a developer, or end user, to easily associate content or services on the device with surfaces or regions of a room. The accuracy of the hotspot marking was tested in an experiment where users selected points marked on a whiteboard using a mobile phone. The distribution of the sample points were analysed and showed that the bulk of the selections were within about 13cm of the target and the distributions were characteristically skewed depending on whether the user came to the target from the left or right. This range is sufficient for prototyping many common ubicomp scenarios based on proximity in a room. To illustrate this approach, we describe the design of a novel mobile application that associates a virtual book library with a region of a room, integrating the additional sensors and actuators of a smartphone with the position sensing of the Kinect. We highlight limitations of this approach and suggest areas for future work. Author Keywords Rapid prototyping, virtual sensors, Kinect, mobile interaction

The emergence of location sharing services is rapidly accelerating the convergence of our online and offline activities. In one direction, Foursquare, Google Latitude, Facebook Places, and related services are enriching real-world venues with the social and semantic connections among online users. In analogy to how clickstreams have been successfully incorporated into traditional web ranking based on content and link analysis, we propose to mine traffic patterns revealed through location sharing services to augment traditional location-based search. Concretely, we study locationbased traffic patterns revealed through location sharing services and find that these traffic patterns can identify semantically related locations. Based on this observation, we propose and evaluate a traffic-driven location clustering algorithm that can group semantically related locations with high confidence. Through experimental study of 12 million locations from Foursquare, we extend this result through supervised location categorization, wherein traffic patterns can be used to accurately predict the semantic category of uncategorized locations. Based on these results, we show how traffic-driven semantic organization of locations may be naturally incorporated into location-based web search.

In this work, we use foursquare check-ins to cluster users via topic modeling, a technique commonly used to classify text documents according to latent “themes”. Here, however, the latent variables which group users can be thought of not as themes but rather as factors which drive check in behaviors, allowing for a qualitative understanding of influences on user check ins. Our model is agnostic of geo-spatial location, time, users ’ friends on social networking sites and the venue categories- we treat the existence of and intricate interactions between these factors as being latent, allowing them to emerge entirely from the data. We instantiate our model on data from New York and the San Francisco Bay Area and find evidence that the model is able to identify groups of people which are of different types (e.g. tourists), communities (e.g. users tightly clustered in space) and interests (e.g. people who enjoy athletics).

Android’s permission system is intended to inform users about the risks of installing applications. When a user installs an application, he or she has the opportunity to review the application’s permission requests and cancel the installation if the permissions are excessive or objectionable. We examine whether the Android permission system is effective at warning users. In particular, we evaluate whether Android users pay attention to, understand, and act on permission information during installation. We performed two usability studies: an Internet survey of 308 Android users, and a laboratory study wherein we interviewed and observed 25 Android users. Study participants displayed low attention and comprehension rates: both the Internet survey and laboratory study found that 17 % of participants paid attention to permissions during installation, and only 3 % of Internet survey respondents could correctly answer all three permission comprehension questions. This indicates that current Android permission warnings do not help most users make correct security decisions. However, a notable minority of users demonstrated both awareness of permission warnings and reasonable rates of comprehension. We present recommendations for improving user attention and comprehension, as well as identify open challenges.