[2603.02711] A Natural Language Agentic Approach to Study Affective Polarization

Computer Science > Artificial Intelligence arXiv:2603.02711 (cs) [Submitted on 3 Mar 2026] Title:A Natural Language Agentic Approach to Study Affective Polarization Authors:Stephanie Anneris Malvicini, Ewelina Gajewska, Arda Derbent, Katarzyna Budzynska, Jarosław A. Chudziak, Maria Vanina Martinez View a PDF of the paper titled A Natural Language Agentic Approach to Study Affective Polarization, by Stephanie Anneris Malvicini and 5 other authors View PDF HTML (experimental) 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, a...