A Natural Language Agentic Approach to Study Affective Polarization
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 pla...
| Authors | Stephanie Anneris Malvicini, Ewelina Gajewska, Arda Derbent et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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