  Basic personality traits are typically assessed through questionnaires. Here
we consider phone-based metrics as a way to asses personality traits. We use
data from smartphones with custom data-collection software distributed to 730
individuals. The data includes information about location, physical motion,
face-to-face contacts, online social network friends, text messages and calls.
The data is further complemented by questionnaire-based data on basic
personality traits. From the phone-based metrics, we define a set of behavioral
variables, which we use in a prediction of basic personality traits. We find
that predominantly, the Big Five personality traits extraversion and, to some
degree, neuroticism are strongly expressed in our data. As an alternative to
the Big Five, we investigate whether other linear combinations of the 44
questions underlying the Big Five Inventory are more predictable. In a tertile
classification problem, basic dimensionality reduction techniques, such as
independent component analysis, increase the predictability relative to the
baseline from $11\%$ to $23\%$. Finally, from a supervised linear classifier,
we were able to further improve this predictability to $33\%$. In all cases,
the most predictable projections had an overweight of the questions related to
extraversion and neuroticism. In addition, our findings indicate that the score
system underlying the Big Five Inventory disregards a part of the information
available in the 44 questions.
