# A Natural Language Agentic Approach to Study Affective Polarization

arXiv: https://arxiv.org/abs/2603.02711 · HTML: https://mlsanity.com/paper/2603.02711/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 11 of 11)

- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks](https://mlsanity.com/paper/2409.19338/) (2024)
- [Affective Polarization and Dynamics of Information Spread in Online Networks](https://mlsanity.com/paper/2310.18553/) (2023)
- [Perspective-taking to Reduce Affective Polarization on Social Media](https://mlsanity.com/paper/2110.05596/) (2021)
- [Understanding Online Polarization Through Human-Agent Interaction in a Synthetic LLM-Based Social Network](https://mlsanity.com/paper/2506.15866/) (2025)
- [In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions](https://mlsanity.com/paper/2412.14414/) (2024)
- [Leveraging a Multi-Agent LLM-Based System to Educate Teachers in Hate Incidents Management](https://mlsanity.com/paper/2506.23774/) (2025)
- [Polarization Detection on Social Networks: dual contrastive objectives for Self-supervision](https://mlsanity.com/paper/2409.07716/) (2024)
- [Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot](https://mlsanity.com/paper/2411.19635/) (2024)
- [Integrated or Segregated? User Behavior Change after Cross-Party Interactions on Reddit](https://mlsanity.com/paper/2410.04923/) (2024)
- [A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs](https://mlsanity.com/paper/2411.12196/) (2024)

## Built on by (most cited first, 2 of 2)

- [Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Framework](https://mlsanity.com/paper/2605.01416/) (2026)
- [RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit](https://mlsanity.com/paper/2606.06027/) (2026)

## Abstract (arXiv)

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.