A multi-terabyte relational database for geo-tagged social network data

László Dobos, János Szüle, Tamás Bodnár, Tamás Hanyecz, Tamás Sebők, Dániel Kondor, Zsófia Kallus, József Stéger, István Csabai and Gábor Vattay

Department of Physics of Complex Systems, Eötvös Loránd University, Pf. 32, H-1518 Budapest, Hungary


Despite their relatively low sampling factor, the freely available, randomly sampled status streams of Twitter are very useful sources of geographically embedded social network data. To statistically analyze the information Twitter provides via these streams, we have collected a year's worth of data and built a multi-terabyte relational database from it. The database is designed for fast data loading and to support a wide range of studies focusing on the statistics and geographic features of social networks, as well as on the linguistic analysis of tweets. In this paper we present the method of data collection, the database design, the data loading procedure and special treatment of geo-tagged and multi-lingual data. We also provide some SQL recipes for computing network statistics.


E-mail: dobos at complex dot elte dot hu

Full Paper

Accepted to IEEE Coginfocom 2013

available on arxiv.org