Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks

2026-05-12

AI Summary

This study addresses the fragmentation in online community polarization research caused by the lack of a unified framework integrating linguistic expression and interaction structure. The authors propose a language-anchored signed network analysis approach that leverages large language models to generate continuous stance scores as signed edge weights. Structural polarization is quantified through Eigen-Sign spectral analysis and partition-based frustration scores, while discourse features—such as toxicity and extreme assertions—are extracted within temporal windows and used in lagged regression models to predict future polarization dynamics. Experiments on Reddit Brexit data demonstrate that the proposed continuously weighted signed network significantly enhances sensitivity to changes in polarization intensity. The two structural polarization measures exhibit strong consistency and complementarity, and linguistic signals effectively forecast subsequent trends in structural polarization.

Abstract

Polarization in online communities is often studied through either language or interaction structure, but the two views are rarely connected in a unified measurement pipeline. Prior work links them by building interaction graphs from human judgments of agreement and disagreement, leaving a gap between language as observed text and structure as an engineered representation of that text. We address this gap with a language-grounded signed-network pipeline that derives continuous signed edge weights from LLM stance scores and quantifies structural polarization using two complementary measures: a spectral Eigen-Sign score and a partition-based frustration score. After normalization, the two measures show substantial agreement while retaining important differences in their sensitivity to edge magnitude. Applying the framework to Reddit Brexit discussions, we analyze how window-level discourse signals, including toxicity, extreme scalar claims, and perplexity, relate to temporal variation in structural polarization. Edge-level and ablation analyses show that continuous, confidence-weighted signed edges reveal intensity-sensitive patterns that are muted under sign-only representations. We further report an exploratory one-step-ahead forecasting analysis suggesting that lagged language signals may contain information about future polarization beyond structural persistence. Together, the results demonstrate how discourse and signed-network structure can be connected in a single framework for measuring and interpreting polarization dynamics over time.

Problem

• polarization
• signed networks
• discourse analysis
• online communities
• language-structure gap

Innovation

• signed interaction networks
• language-grounded polarization
• LLM stance scoring
• structural polarization measures
• continuous signed edges