The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings

Abstract

We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture. Dimensions induced by word differences ( rich – poor) in these spaces correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared associations, which we validate with surveys. Analyzing text from millions of books published over 100 years, we show that the markers of class continuously shifted amidst the economic transformations of the twentieth century, yet the basic cultural dimensions of class remained remarkably stable. The notable exception is education, which became tightly linked to affluence independent of its association with cultivated taste.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 25, 2019
Source ID
10.1177/0003122419877135

Entities

People

  • Austin C. Kozlowski
  • James A. Evans
  • Matt Taddy

Organizations

  • Air Force Office of Scientific Research
  • Amazon
  • Division of Social and Economic Sciences
  • John Templeton Foundation
  • Santa Fe Institute
  • University of Chicago

Tags

Readers

  • Computational Linguistics
  • Economics
  • Neural Network Machine Learning.

Technology Areas

  • Space