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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.</abstract>
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%0 Conference Proceedings
%T Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks
%A Wang, Chenxi
%A Liu, Zongfang
%A Yang, Dequan
%A Chen, Xiuying
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-decoding
%X 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.
%U https://aclanthology.org/2025.coling-main.264/
%P 3913-3923

Markdown (Informal)

[Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks](https://aclanthology.org/2025.coling-main.264/) (Wang et al., COLING 2025)

• Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks (Wang et al., COLING 2025)

ACL

• Chenxi Wang, Zongfang Liu, Dequan Yang, and Xiuying Chen. 2025. Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks. In Proceedings of the 31st International Conference on Computational Linguistics, pages 3913–3923, Abu Dhabi, UAE. Association for Computational Linguistics.