Bayesian nonparametric mixture modeling for temporal dynamics of gender stereotypes

The study of temporal dynamics of gender and ethnic stereotypes is an important topic in many disciplines at the intersection between statistics and social sciences. In this paper we make use of word "embeddings," a common tool in natural language processing and of Bayesian nonparametric mixture modeling for the analysis of temporal dynamics of gender stereotypes in adjectives and occupation over the 20th and 21st centuries in the United States. Our Bayesian nonparametric approach relies on a novel dependent Dirichlet process prior, and it allows for both dynamic density estimation and dynamic clustering of adjective embedding and occupation embedding biases in a hierarchical setting. Posterior inference is performed through a particle Markov chain Monte Carlo algorithm, which is simple and computationally efficient. An application to time-dependent data for adjective embedding bias and for occupation embedding bias shows that our approach enables the quantification of historical trends of gender stereotypes and hence allows to identify how specific adjectives and occupations have become more closely associated with a female rather than male over time.