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Word embeddings quantify 100 years of gender and ethnic stereotypes

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, April 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
16 news outlets
blogs
8 blogs
twitter
268 tweeters
facebook
1 Facebook page
googleplus
4 Google+ users

Citations

dimensions_citation
96 Dimensions

Readers on

mendeley
483 Mendeley
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Title
Word embeddings quantify 100 years of gender and ethnic stereotypes
Published in
Proceedings of the National Academy of Sciences of the United States of America, April 2018
DOI 10.1073/pnas.1720347115
Pubmed ID
Authors

Nikhil Garg, Londa Schiebinger, Dan Jurafsky, James Zou

Abstract

Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts-e.g., the women's movement in the 1960s and Asian immigration into the United States-and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.

Twitter Demographics

The data shown below were collected from the profiles of 268 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 483 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 483 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 127 26%
Student > Master 67 14%
Researcher 61 13%
Student > Bachelor 49 10%
Student > Doctoral Student 28 6%
Other 81 17%
Unknown 70 14%
Readers by discipline Count As %
Computer Science 132 27%
Social Sciences 85 18%
Psychology 44 9%
Engineering 19 4%
Linguistics 18 4%
Other 105 22%
Unknown 80 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 361. 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 14 February 2021.
All research outputs
#46,074
of 17,076,095 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#1,187
of 88,704 outputs
Outputs of similar age
#1,976
of 418,242 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#38
of 952 outputs
Altmetric has tracked 17,076,095 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 88,704 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.3. This one has done particularly well, scoring higher than 98% 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 418,242 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 952 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 96% of its contemporaries.