Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks
Wang, Chenxi Liu, Zongfang Yang, Dequan Chen, Xiuying
Wang, Chenxi
Liu, Zongfang
Yang, Dequan
Chen, Xiuying
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Department
Natural Language Processing
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Conference proceeding
Date
2025
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Language
English
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Abstract
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.
Citation
C. Wang, Z. Liu, D. Yang, and X. Chen, “Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks,” 2025.[Online]. Available: https://aclanthology.org/2025.coling-main.264/
Source
Proceedings - International Conference on Computational Linguistics, COLING
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Keywords
Large Language Models (LLMs), Social opinion networks, Echo chambers, Opinion polarization, Social media simulations
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Publisher
Association for Computational Linguistics