Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks

TL;DR

This paper introduces a language-grounded signed-network pipeline that connects online discourse signals with structural polarization measures, enabling dynamic analysis of polarization in social media discussions.

Contribution

It presents a novel framework that derives continuous signed edge weights from language models and links discourse signals to network polarization metrics.

Findings

Continuous signed edges reveal intensity-sensitive polarization patterns.

Structural polarization measures agree substantially after normalization.

Language signals can predict future polarization beyond structural persistence.

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.