Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks
This work addresses the issue of social media polarization for researchers and policymakers, but it is incremental as it builds on existing models like BCM and FJ with LLM enhancements.
The authors tackled the problem of simulating echo chambers and polarization in social networks by proposing an LLM-based simulation framework, which demonstrated effectiveness in reproducing opinion dynamics and reducing echo chambers through active and passive nudges.
The impact of social media on critical issues such as echo chambers needs to be addressed, as these phenomena can have disruptive consequences for our society. Traditional research often oversimplifies emotional tendencies and opinion evolution into numbers and formulas, neglecting that news and communication are conveyed through text, which limits these approaches. Hence, in this work, we propose an LLM-based simulation for the social opinion network to evaluate and counter polarization phenomena. We first construct three typical network structures to simulate different characteristics of social interactions. Then, agents interact based on recommendation algorithms and update their strategies through reasoning and analysis. By comparing these interactions with the classic Bounded Confidence Model (BCM), the Friedkin Johnsen (FJ) model, and using echo chamber-related indices, we demonstrate the effectiveness of our framework in simulating opinion dynamics and reproducing phenomena such as opinion polarization and echo chambers. We propose two mitigation methods, active and passive nudges, that can help reduce echo chambers, specifically within language-based simulations. We hope our work will offer valuable insights and guidance for social polarization mitigation.