Computational personality assessment
Author(s) / Creator(s)
Stachl, Clemens
Boyd, Ryan L.
Horstmann, Kai T.
Khambatta, Poruz
Matz, Sandra C.
Harari, Gabriella M.
Abstract / Description
Computational methods have increased the objectivity of measures of human behavior and positioned personality science to benefit from the ongoing digital revolution. In this review, we define and discuss computational personality assessment (CPA), a measurement process that uses computational technologies to obtain estimates of personality. We briefly review some of the most promising sources of data currently used for CPA: mobile sensing, digital footprints from social media, images, language, and experience sampling. We present a concise overview of key findings, discuss the promise and opportunities of CPA (e.g., moving towards objective measures of personality, obtaining new insights from big data), and highlight important limitations and challenges in the development and application of CPA (e.g., establishing reliability and validity, selecting appropriate ground truth criterion, assessing affect and cognition, implications for ethics and privacy). We conclude with our perspective on how CPA could change our understanding of individual differences.
Keyword(s)
computational social science personality behavior machine learning psychological assessmentPersistent Identifier
Date of first publication
2021-07-15
Journal title
Personality Science
Volume
2
Article number
Article e6115
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Stachl, C., Boyd, R. L., Horstmann, K. T., Khambatta, P., Matz, S. C., & Harari, G. M. (2021). Computational personality assessment. Personality Science, 2, Article e6115. https://doi.org/10.5964/ps.6115
-
ps.v02.6115.pdfAdobe PDF - 524.02KBMD5: 133cab6a620bf9abc5e2459e55a67fb6
-
There are no other versions of this object.
-
Author(s) / Creator(s)Stachl, Clemens
-
Author(s) / Creator(s)Boyd, Ryan L.
-
Author(s) / Creator(s)Horstmann, Kai T.
-
Author(s) / Creator(s)Khambatta, Poruz
-
Author(s) / Creator(s)Matz, Sandra C.
-
Author(s) / Creator(s)Harari, Gabriella M.
-
PsychArchives acquisition timestamp2022-04-14T11:25:15Z
-
Made available on2022-04-14T11:25:15Z
-
Date of first publication2021-07-15
-
Abstract / DescriptionComputational methods have increased the objectivity of measures of human behavior and positioned personality science to benefit from the ongoing digital revolution. In this review, we define and discuss computational personality assessment (CPA), a measurement process that uses computational technologies to obtain estimates of personality. We briefly review some of the most promising sources of data currently used for CPA: mobile sensing, digital footprints from social media, images, language, and experience sampling. We present a concise overview of key findings, discuss the promise and opportunities of CPA (e.g., moving towards objective measures of personality, obtaining new insights from big data), and highlight important limitations and challenges in the development and application of CPA (e.g., establishing reliability and validity, selecting appropriate ground truth criterion, assessing affect and cognition, implications for ethics and privacy). We conclude with our perspective on how CPA could change our understanding of individual differences.en_US
-
Publication statuspublishedVersion
-
Review statuspeerReviewed
-
CitationStachl, C., Boyd, R. L., Horstmann, K. T., Khambatta, P., Matz, S. C., & Harari, G. M. (2021). Computational personality assessment. Personality Science, 2, Article e6115. https://doi.org/10.5964/ps.6115en_US
-
ISSN2700-0710
-
Persistent Identifierhttps://hdl.handle.net/20.500.12034/5728
-
Persistent Identifierhttps://doi.org/10.23668/psycharchives.6332
-
Language of contenteng
-
PublisherPsychOpen GOLD
-
Is version ofhttps://doi.org/10.5964/ps.6115
-
Is related tohttps://doi.org/10.23668/psycharchives.4973
-
Keyword(s)computational social scienceen_US
-
Keyword(s)personalityen_US
-
Keyword(s)behavioren_US
-
Keyword(s)machine learningen_US
-
Keyword(s)psychological assessmenten_US
-
Dewey Decimal Classification number(s)150
-
TitleComputational personality assessmenten_US
-
DRO typearticle
-
Article numberArticle e6115
-
Journal titlePersonality Science
-
Volume2
-
Visible tag(s)Version of Recorden_US