Measuring Social Media Polarization Using Large Language Models and Heuristic Rules

📝 Original Info

• Title: Measuring Social Media Polarization Using Large Language Models and Heuristic Rules
• ArXiv ID: 2601.00927
• Date: 2026-01-02
• Authors: Jawad Chowdhury, Rezaur Rashid, Gabriel Terejanu

📝 Abstract

Understanding affective polarization in online discourse is crucial for evaluating the societal impact of social media interactions. This study presents a novel framework that leverages large language models (LLMs) and domain-informed heuristics to systematically analyze and quantify affective polarization in discussions on divisive topics such as climate change and gun control. Unlike most prior approaches that relied on sentiment analysis or predefined classifiers, our method integrates LLMs to extract stance, affective tone, and agreement patterns from large-scale social media discussions. We then apply a rule-based scoring system capable of quantifying affective polarization even in small conversations consisting of single interactions, based on stance alignment, emotional content, and interaction dynamics. Our analysis reveals distinct polarization patterns that are event dependent: (i) anticipationdriven polarization, where extreme polarization escalates before wellpublicized events, and (ii) reactive polarization, where intense affective polarization spikes immediately after sudden, high-impact events.

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