Social scientists have recently turned to analyzing text using tools from natural language processing like word embeddings to measure concepts like ideology, bias, and affinity. However, word embeddings are difficult to use in the regression framework familiar to social scientists: embeddings are are neither identified, nor directly interpretable. I offer two advances on standard embedding models to remedy these problems. First, I develop Bayesian Word Embeddings with Automatic Relevance Determination priors, relaxing the assumption that all embedding dimensions have equal weight. Second, I apply work identifying latent variable models to anchor the dimensions of the resulting embeddings, identifying them, and making them interpretable and usable in a regression. I then apply this model and anchoring approach to two cases, the shift in internationalist rhetoric in the American presidents' inaugural addresses, and the relationship between bellicosity in American foreign policy decision-makers' deliberations. I find that inaugural addresses became less internationalist after 1945, which goes against the conventional wisdom, and that an increase in bellicosity is associated with an increase in hostile actions by the United States, showing that elite deliberations are not cheap talk, and helping confirm the validity of the model.
There is a rich legacy of quantitative work in Security Studies, with scholars using regression to make a variety of discoveries about questions of interest. Unfortunately, much of this work pays scant attention to the differences among description, causation, and prediction. This chapter draws on existing work in political science, economics, and statistics to illustrate the distinctions among these approaches and the models and assumptions appropriate for each. The chapter closes with the hope that better quantitative research will lead to improvements in the field of international security and bring everyday methods more in line with the traditions of strong theorizing and effective data-gathering. It also provides resources for the reader to further explore the ideas presented in the chapter.
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