Ubiquitous Internet connectivity enables users to update their Online Social Network profile from any location and at any point in time. These, often geo-tagged, data can be used to provide valuable information to closely located users, both in real time and in aggregated form. However, despite the fact that users publish geo-tagged information, only a small number implicitly reports their base location in their Online Social Network profile. In this paper we present a simple yet effective methodology for identifying a user's key locations, namely her home and work places. We evaluate our methodology with Twitter datasets collected from the country of Netherlands, city of London and Los Angeles county. Furthermore, we combine Twitter and LinkedIn information to construct a work location dataset and evaluate our methodology. Results show that our proposed methodology not only outperforms state-of-the-art methods by at least 30% in terms of accuracy, but also cuts the detection radius at least at half the distance from other methods.

Abstract. Nowadays, smartphones have almost replaced basic feature phones all over the world. Today's smartphones come equipped with an increasing set of embedded sensors, computational and communication resources. All these gave developers the ability to design and implement a wide variety of applications in the domains of healthcare, social networking, safety, environmental monitoring and transportation. This paper presents a novel middleware platform, called Feel the World (FTW) which provides third party programmers, with little phone programming experience, the ability to develop applications that enable people to sense, visualize and share information about the world they live in.

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