Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
The convergence of social and technological networks
Communications of the ACM - Remembering Jim Gray
Dynamical Processes on Complex Networks
Dynamical Processes on Complex Networks
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
Networks: An Introduction
Distance matters: geo-social metrics for online social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Finding your friends and following them to where you are
Proceedings of the fifth ACM international conference on Web search and data mining
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The importance of spatial information in Online Social Networks is increasing at a fast pace. The number of users regularly accessing services from their phones is rising and, therefore, local information is becoming more and more important, for example in targeted marketing and personalized services. In particular, news, from gossips to security alerts, are daily spread across cities through social networks. Content produced by users is consumed by their friends or followers, whose locations can be known or inferred. The spatial location of users' social connections strongly affects the areas where such information will be disseminated. As a consequence, some users can deliver content to a certain geographic area more easily and efficiently than others, for example because they have a larger number of friends in that area. In this paper we present a set of metrics that quantitatively capture the effects of social links on the spreading of information in a given area. We discuss possible application scenarios and we present an initial critical evaluation by means of two datasets from Twitter and Foursquare by discussing a series of case studies.