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Private traits and attributes are predictable from digital records of human behavior

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, March 2013
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  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 45,849)
  • 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)

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Title
Private traits and attributes are predictable from digital records of human behavior
Published in
Proceedings of the National Academy of Sciences of the United States of America, March 2013
DOI 10.1073/pnas.1218772110
Pubmed ID
Authors

Michal Kosinski, David Stillwell, Thore Graepel, Kosinski, Michal, Stillwell, David, Graepel, Thore

Abstract

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.

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Attention Score in Context

This research output has an Altmetric Attention Score of 2122. 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 17 July 2017.
All research outputs
#254
of 8,099,657 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#13
of 45,849 outputs
Outputs of similar age
#4
of 117,672 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#1
of 987 outputs
Altmetric has tracked 8,099,657 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 45,849 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.9. 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 117,672 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 987 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.