Literature DB >> 25583507

Computer-based personality judgments are more accurate than those made by humans.

Wu Youyou1, Michal Kosinski2, David Stillwell3.   

Abstract

Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

Entities:  

Keywords:  artificial intelligence; big data; computational social science; personality judgment; social media

Mesh:

Year:  2015        PMID: 25583507      PMCID: PMC4313801          DOI: 10.1073/pnas.1418680112

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  17 in total

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Journal:  Proc Natl Acad Sci U S A       Date:  2013-03-11       Impact factor: 11.205

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  56 in total

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9.  World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews.

Authors:  Youngseo Son; Sean A P Clouston; Roman Kotov; Johannes C Eichstaedt; Evelyn J Bromet; Benjamin J Luft; H Andrew Schwartz
Journal:  Psychol Med       Date:  2021-06-22       Impact factor: 7.723

10.  Differential ability of network and natural language information on social media to predict interpersonal and mental health traits.

Authors:  Kazuma Mori; Masahiko Haruno
Journal:  J Pers       Date:  2020-08-20
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