Sensing airports’ traffic by mining location sharing social services

Abstract

Location sharing social services are popular among mobile users resulting in a huge social dataset available for researchers to explore. In this paper we consider location sharing social services’ APIs endpoints as “social sensors” that provide data revealing real world interactions, although in some cases, the number of recorded social data can be several orders of magnitude lower compared to the number of real world interactions. In the presented work we focus on check-ins at airports performing two experiments: one analyzing check-in data collected exclusively from Foursquare and another collecting additionally check-in data from Facebook. We compare the two popular location sharing social platforms’ check-ins and we show that for the case of Foursquare these data can be indicative of the passengers’ traffic, while their number is hundreds of times lower than the number of actual traffic observations.