mlsanityData to 7 Oct 2026

Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes

arXiv 1711.08412 ↗First posted 22 Nov 2017cs.CL cs.CYcited by 251

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Abstract (arXiv)

Word embeddings use vectors to represent words such that the geometry between vectors captures semantic relationship between the words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding can be leveraged 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 years of text data with the U.S. Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures global social shifts -- e.g., the women's movement in the 1960s and Asian immigration into the U.S -- 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 powerful new intersection between machine learning and quantitative social science.

Key facts. Cited by 251 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Nov 2017.

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Citing papers per year: 2021: 45; 2022: 50; 2023: 46; 2024: 57; 2025: 26; 2026: 27.

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38 more
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  11. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
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  37. 2021How Does Fine-tuning Affect the Geometry of Embedding Space: A Case Study on Isotropycited by 23
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