A Structural Text-Based Scaling Model for Analyzing Political Discourse
This provides a method for political scientists to analyze discourse and understand polarization, though it is incremental as it builds on existing topic modeling techniques.
The paper tackled the problem of scaling political actors' ideological positions from text data by introducing the Structural Text-Based Scaling (STBS) model, which identified immigration and gun violence as the most polarizing topics in U.S. Senate speeches and found that speaker gender and region significantly influence positions on topics like abortion.
Scaling political actors based on their individual characteristics and behavior helps profiling and grouping them as well as understanding changes in the political landscape. In this paper we introduce the Structural Text-Based Scaling (STBS) model to infer ideological positions of speakers for latent topics from text data. We expand the usual Poisson factorization specification for topic modeling of text data and use flexible shrinkage priors to induce sparsity and enhance interpretability. We also incorporate speaker-specific covariates to assess their association with ideological positions. Applying STBS to U.S. Senate speeches from Congress session 114, we identify immigration and gun violence as the most polarizing topics between the two major parties in Congress. Additionally, we find that, in discussions about abortion, the gender of the speaker significantly influences their position, with female speakers focusing more on women's health. We also see that a speaker's region of origin influences their ideological position more than their religious affiliation.