Crowds, Bluetooth, and Rock-n-Roll. Understanding Music Festival Participant Behavior

TL;DR

This study uses Bluetooth sensors to analyze participant mobility and social interactions at a large music festival, revealing community structures and music preferences through data-driven clustering and modeling.

Contribution

It introduces a novel approach combining Bluetooth sensing and advanced modeling to understand social and music preference structures at large-scale events.

Findings

Identified distinct social communities among festival participants.

Revealed correlations between concert clusters and music genres.

Demonstrated potential for personalized event applications.

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.