{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/word-embeddings-quantify-100-years-of-gender","title":"Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes","arxiv_id":"1711.08412","date":"2017-11-22","proceeding":null,"authors":["Nikhil Garg","Londa Schiebinger","Dan Jurafsky","James Zou"],"abstract":"Word embeddings use vectors to represent words such that the geometry between\nvectors captures semantic relationship between the words. In this paper, we\ndevelop a framework to demonstrate how the temporal dynamics of the embedding\ncan be leveraged to quantify changes in stereotypes and attitudes toward women\nand ethnic minorities in the 20th and 21st centuries in the United States. We\nintegrate word embeddings trained on 100 years of text data with the U.S.\nCensus to show that changes in the embedding track closely with demographic and\noccupation shifts over time. The embedding captures global social shifts --\ne.g., the women's movement in the 1960s and Asian immigration into the U.S --\nand also illuminates how specific adjectives and occupations became more\nclosely associated with certain populations over time. Our framework for\ntemporal analysis of word embedding opens up a powerful new intersection\nbetween machine learning and quantitative social science.","url_abs":"http://arxiv.org/abs/1711.08412v1","url_pdf":"http://arxiv.org/pdf/1711.08412v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"word-embeddings-quantify-100-years-of-gender","repo_url":"https://github.com/nikhgarg/EmbeddingDynamicStereotypes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1711.08412","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}