Predicting personality using novel mobile phone-based metrics

Authors:
Yves-Alexandre de Montjoye;Jordi Quoidbach;Florent Robic;Alex (Sandy) Pentland
Affiliations:
The Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA;Department of Psychology, Harvard University, Cambridge, MA;Ecole Normale Supérieure de Lyon, Lyon, France;The Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA
Venue:
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Year:
2013

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Abstract

The present study provides the first evidence that personality can be reliably predicted from standard mobile phone logs. Using a set of novel psychology-informed indicators that can be computed from data available to all carriers, we were able to predict users' personality with a mean accuracy across traits of 42% better than random, reaching up to 61% accuracy on a three-class problem. Given the fast growing number of mobile phone subscription and availability of phone logs to researchers, our new personality indicators open the door to exciting avenues for future research in social sciences. They potentially enable cost-effective, questionnaire-free investigation of personality-related questions at a scale never seen before.