24 citations · 30 across the 5 of their papers we have counts for
6 papers
Which Text Matters: Handling Model Uncertainty in Text-as-Data Regressions, with an Application to Central Bank Communication
Martin Feldkircher, Bernd Prostmaier, Paul Hofmarcher +1
Social scientists routinely convert text into covariates like topic shares, ideal points or sentiment scores, and insert them into statistical models of interest. This two-step des…
Seeded Poisson Factorization: leveraging domain knowledge to fit topic models
Bernd Prostmaier, Jan Vávra, Bettina Grün +1
Topic models are widely used for discovering latent thematic structures in large text corpora, yet traditional unsupervised methods often struggle to align with pre-defined concept…
A Structural Text-Based Scaling Model for Analyzing Political Discourse
Jan Vávra, Bernd Hans-Konrad Prostmaier, Bettina Grün +1
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 th…
Evolving Voices Based on Temporal Poisson Factorisation
Jan Vávra, Bettina Grün, Paul Hofmarcher
The world is evolving and so is the vocabulary used to discuss topics in speech. Analysing political speech data from more than 30 years requires the use of flexible topic models t…
Revisiting Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech
Paul Hofmarcher, Jan Vávra, Sourav Adhikari +1
Gentzkow, Shapiro and Taddy, Econometrica Vol 87, No 4, 2019 (henceforth GST) use a supervised text-based regression model to assess changes in partisanship in U.S. congressional s…
Model trees with topic model preprocessing: An approach for data journalism illustrated with the WikiLeaks Afghanistan war logs
Thomas Rusch, Paul Hofmarcher, Reinhold Hatzinger +1
The WikiLeaks Afghanistan war logs contain nearly reports of incidents in the US-led Afghanistan war, covering the period from January 2004 to December 2009. The recent gr…