Abstract
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.
Original language | English |
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Title of host publication | Proceedings of Sunbelt 2013 |
Number of pages | 11 |
Publication date | 2013 |
Publication status | Published - 2013 |
Event | 33rd Sunbelt Social Networks Conference of the International Network for Social Network Analysis (INSNA 2013) - Hamburg, Germany Duration: 21 May 2013 → 26 May 2013 http://www.insna.org/sunbelt.html |
Conference
Conference | 33rd Sunbelt Social Networks Conference of the International Network for Social Network Analysis (INSNA 2013) |
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Country/Territory | Germany |
City | Hamburg |
Period | 21/05/2013 → 26/05/2013 |
Internet address |
Keywords
- stat.AP cs.HC