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        <title>Don’t Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism</title>
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    <abstract>The ability of Large Language Models (LLMs) to mimic human behavior triggered a plethora of computational social science research, assuming that empirical studies of humans can be conducted with AI agents instead. Since there have been conflicting research findings on whether and when this hypothesis holds, there is a need to better understand the differences in their experimental designs. We focus on replicating the behavior of social network users with the use of LLMs for the analysis of communication on social networks. First, we provide a formal framework for the simulation of social networks, before focusing on the sub-task of imitating user communication. We empirically test different approaches to imitate user behavior on in English and German. Our findings suggest that social simulations should be validated by their empirical realism measured in the setting in which the simulation components were fitted. With this paper, we argue for more rigor when applying generative-agent-based modeling for social simulation.</abstract>
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%0 Conference Proceedings
%T Don’t Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism
%A Münker, Simon
%A Schwager, Nils
%A Rettinger, Achim
%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 munker-etal-2026-dont
%X The ability of Large Language Models (LLMs) to mimic human behavior triggered a plethora of computational social science research, assuming that empirical studies of humans can be conducted with AI agents instead. Since there have been conflicting research findings on whether and when this hypothesis holds, there is a need to better understand the differences in their experimental designs. We focus on replicating the behavior of social network users with the use of LLMs for the analysis of communication on social networks. First, we provide a formal framework for the simulation of social networks, before focusing on the sub-task of imitating user communication. We empirically test different approaches to imitate user behavior on in English and German. Our findings suggest that social simulations should be validated by their empirical realism measured in the setting in which the simulation components were fitted. With this paper, we argue for more rigor when applying generative-agent-based modeling for social simulation.
%U https://aclanthology.org/2026.eacl-long.51/
%P 1141-1151

Markdown (Informal)

[Don’t Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism](https://aclanthology.org/2026.eacl-long.51/) (Münker et al., EACL 2026)

• Don’t Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism (Münker et al., EACL 2026)

ACL

• Simon Münker, Nils Schwager, and Achim Rettinger. 2026. Don’t Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1141–1151, Rabat, Morocco. Association for Computational Linguistics.