Personality Trait Recognition Based on Smartphone Typing Characteristics in the Wild

Nikola Kovacevic, Christian Holz, Tobias Günther, Markus Gross and Rafael Wampfler

IEEE Transactions on Affective Computing, 2023


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.



  author = {Kovacevic, Nikola and Holz, Christian and G{\"u}nther, Tobias and Gross, Markus and Wampfler, Rafael},
  title = {Personality Trait Recognition Based on Smartphone Typing Characteristics in the Wild},
  journal = {IEEE Transactions on Affective Computing},
  year = {2023},
  publisher = {IEEE},
  doi = {10.1109/TAFFC.2023.3253202},