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@ARTICLE{Chittaranjan_PUC_2012,
         author = {Chittaranjan, Gokul and Blom, Jan and Gatica-Perez, Daniel},
         editor = {Martin, Tom and Starner, Thad},
       keywords = {Big-Five, Lausanne Data Collection Campaign, Personality, Smartphones},
       projects = {Idiap, SONVB, NOKIA},
          title = {Mining Large-Scale Smartphone Data for Personality Studies},
        journal = {Personal and Ubiquitous Computing},
           year = {2012},
       abstract = {In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and self-reported Big-Five personality traits (Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Openness to Experience). Our data stems from smartphones of 117 Nokia N95 smartphone users, collected
over a continuous period of 17 months in Switzerland.
From the analysis, we show that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits. Next, we describe a machine learning method to detect the personality trait of
a user based on smartphone usage. Finally, we study the benefits of using gender-specific models for this task. Apart from a psychological viewpoint, this study facilitates further research on the automated classification
and usage of personality traits for personalizing services
on smartphones.},
            pdf = {https://publications.idiap.ch/attachments/papers/2011/Chittaranjan_PUC_2012.pdf}
}