Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference
Vincent Traaga b, Arnaud Broweta b, Francesco Calabresec d, Frédéric Morlote f g
- aICTEAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium
- bINMA - Pôle en ingénierie mathématique (Department of Mathematical Engineering Faculty of Applied Sciences Université catholique de Louvain Avenue Georges Lemaître, 4 B-1348 Louvain-la-Neuve, Belgium - Belgium)
- cIBM Research, Dublin, Ireland
- dIBM Almaden Research Center [San Jose] (650 Harry Road, San Jose, CA 95120, USA - United States)
- eOrange Laboratories, Issy-les-Moulineaux, France
- fOrange Labs [Issy les Moulineaux] (38-40 Rue du Général Leclerc, Issy les Moulineaux Cedex 9, 92794, France - France)
- gTREC - Theory of networks and communications (France)
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Abstract
The unprecedented amount of data from mobile phones creates new possibilities to analyze various aspects of human behavior. Over the last few years, much effort has been devoted to studying the mobility patterns of humans. In this paper we will focus on unusually large gatherings of people, i.e. unusual social events. We introduce the methodology of detecting such social events in massive mobile phone data, based on a Bayesian location inference framework. More specifically, we also develop a framework for deciding who is attending an event. We demonstrate the method on a few examples. Finally, we discuss some possible future approaches for event detection, and some possible analyses of the detected social events.

