Degrees of separation on a dynamic social network

Social networks are nowadays the most prominent online environments for communication and interaction among in-dividuals, that allow people to communicate more often. In fact, in recent years, most studies have concluded that we are more close now, with the number of degrees of separation becoming smaller for most social networks. These networks are nevertheless continuously changing and evolving, being in fact highly dynamic, with many relations among users not being always active, or even becoming dead links. In this pa-per, we describe the reconstruction of interaction networks from Twitter data, along a given period of time. We re-port detailed statistics for these networks and results about observed degrees of separation. Our results point out that, taking into account just real time interactions, the degrees of separation are higher than those reported for traditional contact networks