Distance matters: geo-social metrics for online social networks

Online Social Networks (OSNs) are increasingly becoming one of the key media of communication over the Internet. The potential of these services as the basis to gather statistics and exploit information about user behavior is appealing and, as a consequence, the number of applications developed for these purposes has been soaring. At the same time, users are now willing to share information about their location, allowing for the study of the role of geographic distance in social ties.

In this paper we present a graph analysis based approach to study social networks with geographic information and new metrics to characterize how geographic distance affects social structure. We apply our analysis to four large-scale OSN datasets: our results show that there is a vast portion of users with short-distance links and that clusters of friends are often geographically close. In addition, we demonstrate that different social networking services exhibit different geo-social properties: OSNs based mainly on location-advertising largely foster local ties and clusters, while services used mainly for news and content sharing present more connections and clusters on longer distances. The results of this work can be exploited to improve many classes of systems and a potential vast number of applications, as we illustrate by means of some practical examples.