2024/10/14 by Jan Vávra, Vávra, Jan, Bernd Prostmaier +5
Computer Science · Social Sciences · #62H99 (Primary) 68U15 #62P25 (Secondary) #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #FOS: Mathematics #G.3 #I.2.7 #Methodology (stat.ME) #Statistics Theory (math.ST) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2410.11897
openalex publication_date 2024/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.