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Authors: Felipe Valadão Goulart and Daniel Oliveira Dantas

Affiliation: Departamento de Computação, Universidade Federal de Sergipe, Brazil

Keyword(s): Personality Traits Extraction, Big Five, Neural Networks.

Abstract: Personality can be defined as a set of psychological features that may determine how to think, act, and feel, as well as may directly influence an individual’s interests. The Big Five model is widely used to describe the main traits of the personality of an individual. This study aims to develop an approach to identify personality traits from keystroke dynamics data using neural networks. We developed a non-intrusive approach to collect keystroke dynamics data from the users and used a self-assessment questionnaire of personality to identify Big Five personality traits. Experiments showed no evidence that the exclusive use of keystroke dynamics characteristics can provide enough information to identify an individual’s personality traits.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Goulart, F. and Dantas, D. (2021). Mapping Personality Traits through Keystroke Analysis. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 474-482. DOI: 10.5220/0010456304740482

@conference{iceis21,
author={Felipe Valadão Goulart. and Daniel Oliveira Dantas.},
title={Mapping Personality Traits through Keystroke Analysis},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2021},
pages={474-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010456304740482},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Mapping Personality Traits through Keystroke Analysis
SN - 978-989-758-509-8
IS - 2184-4992
AU - Goulart, F.
AU - Dantas, D.
PY - 2021
SP - 474
EP - 482
DO - 10.5220/0010456304740482
PB - SciTePress