# A Structural Text-Based Scaling Model for Analyzing Political Discourse

arXiv: https://arxiv.org/abs/2410.11897 · HTML: https://mlsanity.com/paper/2410.11897/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Automatic Differentiation Variational Inference](https://mlsanity.com/paper/1603.00788/) (2016)
- [Text-Based Ideal Points](https://mlsanity.com/paper/2005.04232/) (2020)
- [Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models](https://mlsanity.com/paper/1912.03100/) (2019)
- [Achieving Shrinkage in a Time-Varying Parameter Model Framework](https://mlsanity.com/paper/1611.01310/) (2016)
- [The Dynamic Triple Gamma Prior as a Shrinkage Process Prior for Time-Varying Parameter Models](https://mlsanity.com/paper/2312.10487/) (2023)

## Built on by (most cited first, 2 of 2)

- [Evolving Voices Based on Temporal Poisson Factorisation](https://mlsanity.com/paper/2410.18486/) (2024)
- [Seeded Poisson Factorization: leveraging domain knowledge to fit topic models](https://mlsanity.com/paper/2503.02741/) (2025)

## Abstract (arXiv)

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.