filename     : Kov23a.pdf
entry        : 
conference   : IEEE Transactions on Affective Computing
pages        : 3207 - 3217
year         : 2023
month        : 03
title        : Personality Trait Recognition Based on Smartphone Typing Characteristics in the Wild
subtitle     :
author       : Nikola Kovacevic
booktitle    : 
ISSN/ISBN    : 1949-3045
editor       : 
publisher    : IEEE
publ.place   :
volume       : 14
issue        : 4
language     : English
keywords     : Classification, Affective Computing, Machine Learning, Smartphone Typing Data, Personality Traits
abstract     : As governed by personality trait theory, humans tackle problems differently depending on their long-term behavioral
characteristics. Computational awareness of personality traits fuels affective computing research, which investigates how to reliably
recognize and utilize personality traits. Applications are diverse, including therapy monitoring, learning assistance, and recommender
systems. Data-driven approaches are a promising path forward towards personality-aware human-computer interactions. Thereby, central
challenges are the non-disruptive data acquisition, the time frame over which data must be collected before predictions become accurate,
and the feature-centered data reduction to train reliable and lightweight machine learning models. In this work, we address these
challenges by presenting a fully-automatic feature extraction and machine learning pipeline that makes accurate personality trait
predictions for the widely-used Five Factor Model from passively-collected, short-term smartphone typing data collected from 76
participants (68 university students) in the wild. Our model allows for personality trait assessments after one day of data collection,
demonstrating that, despite being a long-term behavioral trend, personality traits can be inferred accurately from shorter time periods. We
demonstrate that our system can accurately predict personality traits on two levels (low and high) with up to 74.5% accuracy and 0.72
AUC for a single day, and up to 84.5% accuracy and 0.79 AUC after subsequent refinement over 10 weeks.