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

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, January 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#20 of 45,308)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Readers on

mendeley
560 Mendeley
citeulike
1 CiteULike
Title
Computer-based personality judgments are more accurate than those made by humans
Published in
Proceedings of the National Academy of Sciences of the United States of America, January 2015
DOI 10.1073/pnas.1418680112
Pubmed ID
Authors

Wu Youyou, Michal Kosinski, David Stillwell, Youyou W, Kosinski M, Stillwell D

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.

Twitter Demographics

The data shown below were collected from the profiles of 1,026 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 560 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 21 4%
United Kingdom 12 2%
Germany 10 2%
Australia 4 <1%
Brazil 4 <1%
Austria 4 <1%
Finland 3 <1%
Spain 3 <1%
France 3 <1%
Other 22 4%
Unknown 474 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 151 27%
Student > Master 91 16%
Researcher 89 16%
Student > Bachelor 51 9%
Professor > Associate Professor 45 8%
Other 133 24%
Readers by discipline Count As %
Psychology 180 32%
Computer Science 126 23%
Social Sciences 72 13%
Business, Management and Accounting 34 6%
Agricultural and Biological Sciences 34 6%
Other 114 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 1789. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 June 2017.
All research outputs
#382
of 7,944,019 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#20
of 45,308 outputs
Outputs of similar age
#18
of 237,619 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#2
of 946 outputs
Altmetric has tracked 7,944,019 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 45,308 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.3. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 237,619 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 946 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.