A Model of Text for Experimentation in the Social Sciences

Margaret E. Roberts, Brandon Michael Stewart, Edoardo M. Airoldi

Research output: Contribution to journalArticlepeer-review

128 Scopus citations

Abstract

Statistical models of text have become increasingly popular in statistics and computer science as a method of exploring large document collections. Social scientists often want to move beyond exploration, to measurement and experimentation, and make inference about social and political processes that drive discourse and content. In this article, we develop a model of text data that supports this type of substantive research. Our approach is to posit a hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates. In this model, topical prevalence and topical content are specified as a simple generalized linear model on an arbitrary number of document-level covariates, such as news source and time of release, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework. We demonstrate the proposed methodology by analyzing a collection of news reports about China, where we allow the prevalence of topics to evolve over time and vary across newswire services. Our methods quantify the effect of news wire source on both the frequency and nature of topic coverage. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)988-1003
Number of pages16
JournalJournal of the American Statistical Association
Volume111
Issue number515
DOIs
StatePublished - Jul 2 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Causal inference
  • Experimentation
  • High-dimensional inference
  • Social sciences
  • Text analysis
  • Variational approximation

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