Measuring issue-level polarization in congressional speech using community detection-algorithms

The literature on U.S. politics has firmly established an increase in polarization in Congress since the 1970s, arguing that cultural issues are especially polarizing, while economic issues remain less polarized. However, increasing disagreement over the importance of economic issues from welfare state spending to taxation raises the question whether economic discourse has slowly become more polarized, too. This paper investigates issue-level polarization based on issue attention in Congress using speech data and proposes a measurement approach that combines computational text analysis with network analysis. The approach uses speech similarity between individual representatives to construct political networks over time. It measures issue-level polarization within these networks by calculating modularity scores based on the Leiden community detection algorithm. The measurement strategy shows strong patterns of issue-level polarization. This paper makes three contributions. First, it demonstrates that polarization can be studied with text analysis combined with network data. Second, it shows that representatives in Congress increasingly talk about entirely different economic issues. Third, it provides an analytical framework to compare discursive polarization in speech with issue-level polarization.