Tag Archives | Natural Language Processing

Stereotypical Gender Associations in Language Have Decreased Over Time

Jason J. Jones, Mohammad Ruhul Amin, Jessica Kim, Steven Skiena

Sociological Science January 7, 2020
10.15195/v7.a1


Using a corpus of millions of digitized books, we document the presence and trajectory over time of stereotypical gender associations in the written English language from 1800 to 2000. We employ the novel methodology of word embeddings to quantify male gender bias: the tendency to associate a domain with the male gender. We measure male gender bias in four stereotypically gendered domains: career, family, science, and arts. We found that stereotypical gender associations in language have decreased over time but still remain, with career and science terms demonstrating positive male gender bias and family and arts terms demonstrating negative male gender bias. We also seek evidence of changing associations corresponding to the second shift and find partial support. Traditional gender ideology is latent within the text of published English-language books, yet the magnitude of traditionally gendered associations appears to be decreasing over time.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Jason J. Jones: Department of Sociology and Institute for Advanced Computational Science, Stony Brook University
E-mail: Jason.J.Jones@stonybrook.edu

Mohammad Ruhul Amin: Department of Computer Science, Stony Brook University
E-mail: moamin@cs.stonybrook.edu

Jessica Kim: Department of Sociology, Stony Brook University
E-mail: jessica.a.kim@stonybrook.edu

Steven Skiena: Department of Computer Science, Stony Brook University
E-mail: skiena@cs.stonybrook.edu

Acknowledgements: This material is based upon work supported by the National Science Foundation under grants IIS-1546113 and IIS-1927227. The authors would like to thank Stony Brook Research Computing and Cyberinfrastructure as well as the Institute for Advanced Computational Science at Stony Brook University for access to the highperformance SeaWulf computing system, which was made possible by a $1.4 million National Science Foundation grant (#1531492).

  • Citation: Jones, Jason J., Mohammad Ruhul Amin, Jessica Kim, and Steven Skiena. 2019. “Stereotypical Gender Associations in Language Have Decreased Over Time.” Sociological Science 7: 1-35.
  • Received: August 13, 2019
  • Accepted: October 31, 2019
  • Editors: Jesper Sørensen, Sarah Soule
  • DOI: 10.15195/v7.a1


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