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.

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de Montjoye , Yves-Alexandre; Quoidbach, Jordi; Robic , Florent; Pentland , Alex

Predicting personality using novel mobile phone-based metrics

2013
itemDefault 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.
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Predicting personality using novel mobile phone-based metrics
de Montjoye , Yves-Alexandre; Quoidbach, Jordi; Robic , Florent; Pentland , Alex
In Social Computing, Behavioral-Cultural Modeling and Prediction
Berlín (Germany): Springer, 2013
p. 48 - 55

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