Identifying anomalous social contexts from mobile proximity data using binomial mixture models

Authors:
Eric Malmi;Juha Raitio;Oskar Kohonen;Krista Lagus;Timo Honkela
Affiliations:
Department of Information and Computer Science, Aalto University, Aalto, Finland;Department of Information and Computer Science, Aalto University, Aalto, Finland;Department of Information and Computer Science, Aalto University, Aalto, Finland;Department of Information and Computer Science, Aalto University, Aalto, Finland;Department of Information and Computer Science, Aalto University, Aalto, Finland
Venue:
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Year:
2012

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Abstract

Mobile proximity information provides a rich and detailed view into the social interactions of mobile phone users, allowing novel empirical studies of human behavior and context-aware applications. In this study, we apply a statistical anomaly detection method based on multivariate binomial mixture models to mobile proximity data from 106 users. The method detects days when a person's social context is unexpected, and it provides a clustering of days based on the contexts. We present a detailed analysis regarding one user, identifying days with anomalous contexts, and potential reasons for the anomalies. We also study the overall anomalousness of people's social contexts. This analysis reveals a clear weekly oscillation in the predictability of the contexts and a weekend-like behavior on public holidays.