Linking Scalar-Intensity Language to Structural Polarization with Validated Signed-Network Measures
The study developed a pipeline connecting language to structural polarization dynamics using signed networks. It uses an LLM to create confidence-weighted, signed edges from conversations and measures like Eigen-Sign and frustration scores to quantify polarization over time. Findings show extreme scalar claims negatively correlate with normalized Eigen-Sign scores, linking specific language intensity directly to network structure.