How do online news habits affect political polarization?
What Contributes to Affective Polarization in Networked Online Environments? Evidence from an Agent-Based Model
April 11, 2025
https://arxiv.org/pdf/2504.07610This paper explores how affective polarization (strong dislike for opposing political parties) spreads in online environments. It uses an agent-based model to simulate how news consumption and sharing, influenced by individual biases and social networks, affect political polarization.
Key points for LLM-based multi-agent systems:
- Agent-based modeling is a valuable tool for simulating complex social phenomena like affective polarization. LLM agents can represent individuals with varying biases and behaviors.
- Affective asymmetry, where negative reactions to opposing views outweigh positive reactions to agreeing views, is a key driver of polarization. LLM agents could be programmed to exhibit this asymmetry.
- Network structure and the composition of influential "elite" agents strongly impact polarization. This highlights the importance of network topology and elite agent behavior in LLM-based multi-agent simulations.
- Homogeneous networks (echo chambers) lead to rapid in-group loyalty but also broader dissemination of information. Balanced networks increase cross-cutting exposure, which can intensify hostility if agents react more strongly to opposing viewpoints. This is relevant for designing interaction patterns in multi-agent LLM applications.
- The model focuses on confirmation bias where agents preferentially share content aligning with their views, a behavior easily replicated with LLMs by training on biased datasets or incorporating bias in prompting/fine-tuning.
- The research suggests the need to model not just content, but also the credibility of sources and doses of exposure, which is highly relevant for representing nuanced information environments with LLMs.