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Word embeddings use vectors to represent words such that the geometry betweenvectors captures semantic relationship between the words. In this paper, wedevelop a framework to demonstrate how the temporal dynamics of the embeddingcan be leveraged to quantify changes in stereotypes and attitudes toward womenand ethnic minorities in the 20th and 21st centuries in the United States. Weintegrate 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 andoccupation 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 moreclosely associated with certain populations over time. Our framework fortemporal analysis of word embedding opens up a powerful new intersectionbetween machine learning and quantitative social science.

Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes