A Natural Language Agentic Approach to Study Affective Polarization

2026-03-03

AI Summary

Existing research on affective polarization is constrained by the scarcity of real-world data, high subjectivity in annotation, and the absence of a unified computational framework. This work proposes the first large language model (LLM)-driven multi-agent simulation platform that leverages natural language to generate context-aware virtual users, enabling the modeling of complex social interactions and emotional dynamics within synthetic social environments. The framework supports configurable, multi-level polarization scenarios, substantially enhancing reproducibility and cross-study comparability. Through experiments, the platform successfully replicates and extends key polarization phenomena documented in social psychology, demonstrating its effectiveness and potential for efficiently and flexibly investigating the dynamic mechanisms underlying affective polarization.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Affective polarization has been central to political and social studies, with growing focus on social media, where partisan divisions are often exacerbated. Real-world studies tend to have limited scope, while simulated studies suffer from insufficient high-quality training data, as manually labeling posts is labor-intensive and prone to subjective biases. The lack of adequate tools to formalize different definitions of affective polarization across studies complicates result comparison and hinders interoperable frameworks. We present a multi-agent model providing a comprehensive approach to studying affective polarization in social media. To operationalize our framework, we develop a platform leveraging large language models (LLMs) to construct virtual communities where agents engage in discussions. We showcase the potential of our platform by (1) analyzing questions related to affective polarization, as explored in social science literature, providing a fresh perspective on this phenomenon, and (2) introducing scenarios that allow observation and measurement of polarization at different levels of granularity and abstraction. Experiments show that our platform is a flexible tool for computational studies of complex social dynamics such as affective polarization. It leverages advanced agent models to simulate rich, context-sensitive interactions and systematically explore research questions traditionally addressed through human-subject studies.

Problem

• affective polarization
• social media
• training data
• labeling bias
• interoperable frameworks

Innovation

• multi-agent simulation
• large language models
• affective polarization
• computational social science
• virtual communities