Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding

Abstract

Topic modeling provides a valuable method for identifying the linguistic contexts that surround social institutions or policy domains. This article uses Latent Dirichlet Allocation (LDA) to analyze how one such policy domain, government assistance to artists and arts organizations, was framed in almost 8000 articles. These comprised all articles that referred to government support for the arts in the U.S. published in five U.S. newspapers between 1986 and 1997—a period during which such assistance, once noncontroversial, became a focus of contention. We illustrate the strengths of topic modeling as a means of analyzing large text corpora, discuss the proper choice of models and interpretation of model results, describe means of validating topic-model solutions, and demonstrate the use of topic models in combination with other statistical tools to estimate differences between newspapers in the prevalence of different frames. Throughout, we emphasize affinities between the topic-modeling approach and such central concepts in the study of culture as framing, polysemy, heteroglossia, and the relationality of meaning.