The Structural Topic Model and Applied Social Science∗

We develop the Structural Topic Model which provides a general way to incorporate corpus structure or document metadata into the standard topic model. Document-level covariates enter the model through a simple generalized linear model framework in the prior distributions controlling either topical prevalence or topical content. We demonstrate the model’s use in two applied problems: the analysis of open-ended responses in a survey experiment about immigration policy, and understanding differing media coverage of China’s rise. 1 Topic Models and Social Science Over the last decade probabilistic topic models, such as Latent Dirichlet Allocation (LDA), have become a common tool for understanding large text corpora [1].1 Although originally developed for descriptive and exploratory purposes, social scientists are increasingly seeing the value of topic models as a tool for measurement of latent linguistic, political and psychological variables [2]. The defining element of this work is the presence of additional document-level information (e.g. author, partisan affiliation, date) on which variation in either topical prevalence or topical content is of theoretic interest.2 As a practical matter, this generally involves running an off-the-shelf implementation of LDA and then performing a post-hoc evaluation of variation with a covariate of interest. A better alternative to post-hoc comparisons is to build the additional information about the structure of the corpus into the model itself by altering the prior distributions to partially pool information amongst similar documents. Numerous special cases of this framework have been developed for particular types of corpus structure affecting both topic prevalence (e.g. time [3], author [4]) and topical content (e.g. ideology [5], geography [6]). Applied users have been slow to adopt these models because it is often difficult to find a model that exactly fits their specific corpus. We develop the Structural Topic Model (STM) which accommodates corpus structure through document-level covariates affecting topical prevalence and/or topical content. The central idea is to ∗Prepared for the NIPS 2013 Workshop on Topic Models: Computation, Application, and Evaluation. A forthcoming R package implements the methods described here. † These authors contributed equally. We assume a general familiarity with LDA throughout (see [1] for a review) By “topical prevalence” we mean the proportion of document devoted to a given topic. By “topical content” we mean the rate of word use within a given topic.