Tag Archives | Word Embeddings

Tracing Politicization in the U.S. Public Sphere, 1980–2024

Terrence Ting-Yen Chen

Sociological Science October 27, 2026
10.15195/v13.a46


Although many argue that American society has become overly politicized, social scientists have yet to systematically measure “politicization”—the process by which issues are framed as political through their association with recognizable political divisions in public discourse. This article develops a new measure of politicization using à la carte word embedding models trained on 1.35 million articles from three national newspapers. I trace the historical politicization of 25 issue domains in the United States from 1980 to 2024 by examining the extent to which specific issues are discursively associated with ideological labels (liberal and conservative) and partisan markers (Democrat and Republican). The analysis yields three main findings. First, politicization in the American public sphere has increased substantially over the past four decades, and this increase has extended beyond traditionally political issues into previously less politicized aspects of social life. Second, the discursive locus of political conflict has shifted over time: race and immigration, gender and family, and legal institutions have become more politicized than economic issues, which had been among the most politicized domains in the 1980s. Third, the boundary between the political and the less-political remains consequential, as some important areas of social life such as the home, financial markets, and outer space remain relatively unpoliticized in public discourse today. By studying politicization as a phenomenon in its own right, this research contributes to cultural and political sociology by treating “politics” as a dynamic category whose boundaries are continuously renegotiated, while also reorienting scholars of political division to shift their focus from public opinion and formal institutions toward the “public culture” through which social conflict and cohesion are represented and experienced.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Terrence Ting-Yen Chen: Department of Sociology, New York University
E-mail: tychen@nyu.edu

Acknowledgments: I appreciate the invaluable feedback and support from Bart Bonikowski, Alex Barnard, Paul DiMaggio, BK Lee, Carly Knight, Delia Baldassarri, Mike Hout, my cohort mates in the New York University (NYU) Research and Writing Seminar, as well as participants in the NYU Ideology & Identity Project Graduate Student Workshop, Carly Knight’s student research group, and the roundtable at the 2024 American Sociological Association Annual Conference. I thank the Editor-in-Chief, the Deputy Editor, and the anonymous reviewers of Sociological Science for their careful reading and helpful suggestions. I am also grateful to the NYU library staff and the technical consultants at ProQuest TDM Studio for their assistance with data access and analysis. Corresponding author: Terrence Ting-Yen Chen, E-mail: tychen@nyu.edu


Supplemental Materials

Reproducibility Package: The code and data to reproduce all results reported in the article are available at https://github.com/TerrenceChen/Politicization\_paper. The full text data of the newspapers cannot be uploaded to a public location because the data can only be accessed through institutional access and user registration at ProQuest TDM Studio (https://tdmstudio.proquest.com). The README page in the reproduction package details how to gain access to the full text data used in this article.


  • Citation: Chen, Terrence Ting-Yen. 2026. “Tracing Politicization in the U.S. Public Sphere, 1980–2024” Sociological Science 13: 1204-1240.
  • Received: June 25, 2026
  • Accepted: September 15, 2026
  • Editors: Ari Adut, Kieran Healy
  • DOI: 10.15195/v13.a46


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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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