Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding
Introduction
This article addresses three puzzles. The first is methodological. How can researchers analyze large quantities of textual data efficiently and effectively? Specifically, how can we capture the information we need, reduce its complexity, and provide interpretations that are substantively plausible and statistically validated? We present topic modeling and, specifically, Latent Dirichlet Allocation (LDA) as a promising solution to these challenges.
The second puzzle is theoretical: The sociology of culture has long been theory-rich and methods poor. Sociologists who study culture have generated numerous theoretical insights and developed concepts that promise a deep understanding of cultural change. Yet they have often lacked the means to make such concepts operational (Mohr, 1998). We suggest that topic modeling may provide a way to do just that for such central concepts as framing, polysemy, heteroglossia, and the relationality of meaning.
The third is part of an ongoing study by the first author of the dramatic politicization of government support for arts organizations and artists in the late 1980s after a two-decade honeymoon. This article addresses how press coverage of public funding of the arts evolved from 1986 to 1997, a period that spans the beginning and end of the period of most acute contention.
Section snippets
Background: the crisis of public arts support in the U.S.
U.S. municipalities supported museums as early as the nineteenth century and financed bands and orchestras into the 1920s; the Roosevelt administration created a federal jobs program for artists during the Great Depression; several states established arts councils before 1965, and arts organizations receive substantial tax subsidy through the charitable deduction.1
Press accounts of public support for the arts in the U.S.
Did the tone of press coverage of government arts support change during the 1980s and 1990s? Did changes, if any, precede or coincide with political attacks on the NEA? Were some newspapers more likely to frame arts support in a positive light, whereas others were more likely to frame it as offensive or contentious? In short, how did the press respond to, participate in or contribute to the NEA's political woes?
Topic modeling: an inductive relational approach to the study of culture
Textual analysis has always been a central part of the study of culture. The digitization of huge quantities of text has raised the stakes by enabling scholars to launch more ambitious projects, while requiring development of new, more powerful, analytic tools. As a leading text on content analysis puts it, digitization shifts “the bottleneck of content analysis from the costs of access and tedious human coding to the need for good theory, sound methodology, and software…” (Krippendorf, 2004,
Topic modeling renders operational central ideas in the sociology of culture
Although automated approaches to textual analysis are increasingly plentiful, specific affinities between the topic-modeling algorithm and key ideas in the sociology of culture produce a strong fit between theory and method. In this section, we discuss the ways in which topic models enable scholars to render operational ideas about the relationality of meaning, heteroglossia, and framing.
Substantive application: did news outlets vary in their use of conflict frames?
For students of culture, producing a topic model is not an end in itself, but rather the first step in a longer process of interpretation and analysis. In the current case, by construing Topic 2 (arts controversies) and Topic 8 (polarization) as alternative frames for depicting conflict over arts grants—in one case treating controversial grants as a distinct problem, in the other treating arts funding as one of many related “social issues”—we can ask whether different news outlets varied in
Conclusions and further work
This article describes how to use probabilistic topic models of newspaper articles to study cultural trends, moods, and depictions. We studied press coverage of government grants supporting the arts between 1986 and 1997. During this period, such grants became controversial and the National Endowment for the Arts, the federal agency that made many of them, faced fierce attack from Republicans and conservative social movement organizations. In this section we first summarize key substantive
Acknowledgements
Research support from Princeton University and sabbatical support for the first author from the Russell Sage Foundation are gratefully acknowledged, as is the assistance of Brian Steensland in gathering parts of the textual data. Support from the Rockefeller Foundation and the Andrew W. Mellon Foundation (through a grant to Princeton's Center for Arts and Cultural Policy Studies) for data collection, research and sabbatical support from Princeton University and the Russell Sage Foundation and
Paul DiMaggio is A. Barton Hepburn Professor of Sociology and Public Affairs at Princeton University. His interests include patterns of cultural participation, the organization of the Internet, formal and statistical methods of cultural analysis, and the impact of social networks on social inequality.
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Paul DiMaggio is A. Barton Hepburn Professor of Sociology and Public Affairs at Princeton University. His interests include patterns of cultural participation, the organization of the Internet, formal and statistical methods of cultural analysis, and the impact of social networks on social inequality.
Manish Nag is a PhD candidate in the Department of Sociology at Princeton University. His research utilizes computational social science innovations in text analysis and social network analysis to understand cultural change in media and academic discourses, as well as change and resilience in global networks of people, goods and ideas.
David Blei is an associate professor of Computer Science at Princeton University. His research focuses on probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. He works on a variety of applications, including text, images, music, social networks, and scientific data.