  In this paper we present a study of sensing and analyzing an offline social
network of participants at a large-scale music festival (8 days, 130,000+
participants). We place 33 fixed-location Bluetooth scanners in strategic spots
around the festival area to discover Bluetooth-enabled mobile phones carried by
the participants, and thus collect spatio-temporal traces of their mobility and
interactions. We subsequently analyze the data on two levels. On the micro
level, we run a community detection algorithm to reveal a variety of groups the
festival participants form. On the macro level, we employ an Infinite
Relational Model (IRM) in order to recover the structure of the social network
related to participants' music preferences. The obtained structure in the form
of clusters of concerts and participants is then interpreted using
meta-information about music genres, band origins, stages, and dates of
performances. We show that most of the concerts clusters can be described by
one or more of the meta-features, effectively revealing preferences of
participants (e.g. a cluster of US bands) and discuss the significance of the
findings and the potential and limitations of the used method. Finally, we
discuss the possibility of employing the described method and techniques for
creating user-oriented applications and extending the sensing capabilities
during large-scale events by introducing user involvement.
