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Computational personality assessment

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Computational personality assessment. / Stachl, Clemens; Boyd, Ryan L; Horstmann, Kai T. et al.
In: Personality Science, Vol. 2, No. 1, e6115, 15.07.2021.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Stachl, C, Boyd, RL, Horstmann, KT, Khambatta, P, Matz, SC & Harari, GM 2021, 'Computational personality assessment', Personality Science, vol. 2, no. 1, e6115. https://doi.org/10.5964/ps.6115

APA

Stachl, C., Boyd, R. L., Horstmann, K. T., Khambatta, P., Matz, S. C., & Harari, G. M. (2021). Computational personality assessment. Personality Science, 2(1), Article e6115. https://doi.org/10.5964/ps.6115

Vancouver

Stachl C, Boyd RL, Horstmann KT, Khambatta P, Matz SC, Harari GM. Computational personality assessment. Personality Science. 2021 Jul 15;2(1):e6115. doi: 10.5964/ps.6115

Author

Stachl, Clemens ; Boyd, Ryan L ; Horstmann, Kai T. et al. / Computational personality assessment. In: Personality Science. 2021 ; Vol. 2, No. 1.

Bibtex

@article{31a277c50c6142a8b24c5961df240863,
title = "Computational personality assessment",
abstract = "Innovations in computational methods for the representation and analysis of data have drastically increased the objectivity, reliability, and the practical implications of research conducted throughout most scientific pursuits. Our rapidly-emerging potential to transform digital data into objective measures of human behavior, thoughts, and feelings has perfectly positioned personality science as a critical discipline that will benefit from today{\textquoteright}s ongoing digital revolution. Here, we briefly review and discuss some of the most promising sources of data used for computational personality assessment: mobile sensing, online social media, images, language use, and experience sampling. We present a concise overview of key findings, discuss the potential and promise of computational personality assessment, and highlight important remaining questions in their development and application. We conclude with an optimistic outlook on how computational assessment could fuel the transition from personality research to personality science.",
author = "Clemens Stachl and Boyd, {Ryan L} and Horstmann, {Kai T.} and Poruz Khambatta and Matz, {Sandra C.} and Harari, {Gabriella M.}",
year = "2021",
month = jul,
day = "15",
doi = "10.5964/ps.6115",
language = "English",
volume = "2",
journal = "Personality Science",
issn = "2700-0710",
publisher = "PsychOpen",
number = "1",

}

RIS

TY - JOUR

T1 - Computational personality assessment

AU - Stachl, Clemens

AU - Boyd, Ryan L

AU - Horstmann, Kai T.

AU - Khambatta, Poruz

AU - Matz, Sandra C.

AU - Harari, Gabriella M.

PY - 2021/7/15

Y1 - 2021/7/15

N2 - Innovations in computational methods for the representation and analysis of data have drastically increased the objectivity, reliability, and the practical implications of research conducted throughout most scientific pursuits. Our rapidly-emerging potential to transform digital data into objective measures of human behavior, thoughts, and feelings has perfectly positioned personality science as a critical discipline that will benefit from today’s ongoing digital revolution. Here, we briefly review and discuss some of the most promising sources of data used for computational personality assessment: mobile sensing, online social media, images, language use, and experience sampling. We present a concise overview of key findings, discuss the potential and promise of computational personality assessment, and highlight important remaining questions in their development and application. We conclude with an optimistic outlook on how computational assessment could fuel the transition from personality research to personality science.

AB - Innovations in computational methods for the representation and analysis of data have drastically increased the objectivity, reliability, and the practical implications of research conducted throughout most scientific pursuits. Our rapidly-emerging potential to transform digital data into objective measures of human behavior, thoughts, and feelings has perfectly positioned personality science as a critical discipline that will benefit from today’s ongoing digital revolution. Here, we briefly review and discuss some of the most promising sources of data used for computational personality assessment: mobile sensing, online social media, images, language use, and experience sampling. We present a concise overview of key findings, discuss the potential and promise of computational personality assessment, and highlight important remaining questions in their development and application. We conclude with an optimistic outlook on how computational assessment could fuel the transition from personality research to personality science.

U2 - 10.5964/ps.6115

DO - 10.5964/ps.6115

M3 - Journal article

VL - 2

JO - Personality Science

JF - Personality Science

SN - 2700-0710

IS - 1

M1 - e6115

ER -