Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference

Vincent Traag (1) , Arnaud Browet (1) , Francesco Calabrese (2) , Frédéric Morlot (3, 4)

1 INMA - Pôle en ingénierie mathématique
2 IBM Almaden Research Center [San Jose]
3 TREC - Theory of networks and communications
4 Orange Labs [Issy les Moulineaux]

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.

Domaines: Modélisation et simulation; Statistiques [math.ST]; Théorie [stat.TH]; Probabilités [math.PR]

Vincent Traag, Arnaud Browet, Francesco Calabrese, Frédéric Morlot. Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference. 2011. ⟨hal-00627122⟩