Ideology from topic mixture statistics: Inference method and example application to carbon tax public opinion

Abstract

Political opposition to fiscal climate policy, such as a carbon tax, typically appeals to fiscal conservative ideology. Here, we ask to what extent public opposition to the carbon tax in Canada is, in fact, ideological in origin. As an object of study, ideology is a latent belief structure over a set of issue topics - and in particular their relationships - as revealed through stated opinions. Ideology is thus amenable to a generative modeling approach within the text-as-data paradigm. We use the Structural Topic Model, which generates word content from a set of latent topics and mixture weights placed on them. We fit the model to open-ended survey responses of Canadians elaborating on their support of or opposition to a carbon tax, then use it to infer the set of mixture weights used by each response. We demonstrate this set, moreso than the observed word use, serves efficient discrimination of opposition from support, with near-perfect accuracy on held-out data. We then operationalize ideology as the empirical distribution of inferred topic mixture weights. We propose and use an evaluation of ideology-driven beliefs based on four statistics of this distribution capturing the specificity, variability, expressivity, and alignment of the underlying ideology. We find that the ideology behind responses from respondents who opposed the carbon tax is more specific and aligned, much less expressive, and of similar variability as compared with those who support the tax. We discuss the implications of our results for climate policy and of broad application of our approach in social science.