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        <title>An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents</title>
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        <name type="personal">
            <namePart type="given">Vera</namePart>
            <namePart type="family">Demberg</namePart>
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        <name type="personal">
            <namePart type="given">Lluís</namePart>
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    <abstract>Large Language Models (LLMs) increasingly mediate our social, cultural, and political interactions. While they can simulate some aspects of human behavior and decision-making, it is still underexplored whether repeated interactions with other agents amplify their biases or lead to exclusionary behaviors. To this end, we study Chirper.ai—an LLM-driven social media platform—analyzing 7M posts and interactions among 32K LLM agents over a year. We start with homophily and social influence among LLMs, learning that similar to humans’, their social networks exhibit these fundamental phenomena. Next, we study the toxic language of LLMs, its linguistic features, and their interaction patterns, finding that LLMs show different structural patterns in toxic posting than humans. After studying the ideological leaning in LLMs posts, and the polarization in their community, we focus on how to prevent their potential harmful activities. We present a simple yet effective method, called Chain of Social Thought (CoST), that reminds LLM agents to avoid harmful posting.</abstract>
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%0 Conference Proceedings
%T An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents
%A Hashemi, Farnoosh
%A Macy, Michael
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F hashemi-macy-2026-empirical
%X Large Language Models (LLMs) increasingly mediate our social, cultural, and political interactions. While they can simulate some aspects of human behavior and decision-making, it is still underexplored whether repeated interactions with other agents amplify their biases or lead to exclusionary behaviors. To this end, we study Chirper.ai—an LLM-driven social media platform—analyzing 7M posts and interactions among 32K LLM agents over a year. We start with homophily and social influence among LLMs, learning that similar to humans’, their social networks exhibit these fundamental phenomena. Next, we study the toxic language of LLMs, its linguistic features, and their interaction patterns, finding that LLMs show different structural patterns in toxic posting than humans. After studying the ideological leaning in LLMs posts, and the polarization in their community, we focus on how to prevent their potential harmful activities. We present a simple yet effective method, called Chain of Social Thought (CoST), that reminds LLM agents to avoid harmful posting.
%U https://aclanthology.org/2026.eacl-long.344/
%P 7327-7351

Markdown (Informal)

[An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents](https://aclanthology.org/2026.eacl-long.344/) (Hashemi & Macy, EACL 2026)

• An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents (Hashemi & Macy, EACL 2026)

ACL

• Farnoosh Hashemi and Michael Macy. 2026. An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7327–7351, Rabat, Morocco. Association for Computational Linguistics.