Measuring polarization in epistemic social networks: The case of the US 2024 presidential elections

This paper advances a methodological framework for the study of policy polarization. We conceptualize polarization as the structure of relationships among political and epistemic actors, measurable through the similarity of their policy statements. To generate these relational data, large language models (LLMs) are employed as scalable tools for extracting, summarizing, and synthesizing policy statements or positions from diverse textual sources, including debates, party platforms, interviews, and think tank publications. The resulting corpus is embedded into a shared vector space, and similarity measures between statements provide the basis for constructing multimodal networks that connect policy statements through semantic similarity and link them to candidates and think tanks. These networks enable the application of social network analysis, such as correspondence analysis, influence modeling, and structural comparison, to model alignments, divergences, and patterns of polarization. The framework contributes a replicable and extensible approach to analyzing policy polarization, integrating computational text processing with network-analytic models to capture the relational dynamics of political discourse.