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Using big data to advance personality theory


Bleidorn, Wiebke; Hopwood, Christopher J; Wright, Aidan G C (2017). Using big data to advance personality theory. Current Opinion in Behavioral Sciences, 18:79-82.

Abstract

Big data has led to remarkable advances in society. One of the most exciting applications in psychological science has been the development of computer-based assessment tools to assess human behavior and personality traits. Thus far, machine learning approaches to personality assessment have been focused on maximizing predictive validity, but have been underused to advance our understanding of personality. In this paper, we review recent machine learning studies of personality and discuss recommendations for how big data and machine learning research can be used to advance personality theory.

Abstract

Big data has led to remarkable advances in society. One of the most exciting applications in psychological science has been the development of computer-based assessment tools to assess human behavior and personality traits. Thus far, machine learning approaches to personality assessment have been focused on maximizing predictive validity, but have been underused to advance our understanding of personality. In this paper, we review recent machine learning studies of personality and discuss recommendations for how big data and machine learning research can be used to advance personality theory.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Scopus Subject Areas:Life Sciences > Cognitive Neuroscience
Health Sciences > Psychiatry and Mental Health
Life Sciences > Behavioral Neuroscience
Uncontrolled Keywords:Behavioral Neuroscience, Psychiatry and Mental health, Cognitive Neuroscience
Language:English
Date:1 December 2017
Deposited On:08 Sep 2021 14:34
Last Modified:25 Apr 2024 01:39
Publisher:Elsevier
ISSN:2352-1546
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.cobeha.2017.08.004
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