---
title: "Don't Trust Generative Agents to Mimic Communication on Social"
description: "The authors examine this for social networks, where simulation could reveal how platform design… 39 references, cited by 3 papers here."
authors: ["Simon Münker", "Nils Schwager", "Achim Rettinger"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.eacl-long.51/"
doi: "10.18653/v1/2026.eacl-long.51"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/munker-2026-don-t-trust-generative-agents/"
---
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# Don't Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism

**Authors:** [Simon Münker](https://md.research.elamur.ai/free/en/authors/simon-munker/), [Nils Schwager](https://md.research.elamur.ai/free/en/authors/nils-schwager/) and [Achim Rettinger](https://md.research.elamur.ai/free/en/authors/achim-rettinger/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-long.51/) · [PDF](https://aclanthology.org/2026.eacl-long.51.pdf) · [DOI 10.18653/v1/2026.eacl-long.51](https://doi.org/10.18653/v1/2026.eacl-long.51) · [OpenAlex](https://openalex.org/W7140086128)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/munker-2026-don-t-trust-generative-agents/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM-driven agents are increasingly used as stand-ins for people in computational social science. However, studies disagree on whether they reproduce human behaviour faithfully. The authors examine this for social networks, where simulation could reveal how platform design choices such as ranking or filtering affect discourse, a question relevant to regulation like the EU Digital Services Act.

They propose a formal model of "Twins of Online Social Networks" (TWONs) with two parts:
- **Agent behaviour:** a state-update function and a message-producing function for each user.
- **Platform mechanics:** a function that curates the messages each user receives.

Any downstream discourse measure, such as outrage, should be reported together with losses that quantify how realistic the simulated behaviour is. As a case study, they fit and benchmark several ways of imitating highly active X users in English and German across posting, replying, and reply-likelihood tasks.

Their central claim is that a simulation's empirical realism must be measured in the same setting where its components were fitted. Without this validation, conclusions drawn from generative-agent simulations should not be trusted.

## Key points
- **Framework:** TWONs separate user behaviour *b* from network mechanics *r*. The fitting losses L_b and L_r act as confidence scores that should accompany any simulated finding.
- **Benchmarked tasks:**
  - Posting (Creator role): free text, cross-entropy loss, evaluated with BLEU and n-gram metrics.
  - Replying (Reactor role): free text, same loss and metrics as posting.
  - Reply likelihood (Reactor role): probability output, binary cross-entropy loss, evaluated with F1 and accuracy.
- **Data:** X data in English and German, covering political figures' posts and public replies from regular users. Evaluation is limited to the most active users. The excerpt omits the detailed numeric results.
- **Scope limits:** only single-turn behaviour is simulated, with no multi-turn or polarization dynamics. There is also no qualitative study of community-specific argumentation styles.
- **Ethics and availability:** for privacy reasons, neither the dataset nor the per-user models are released, only aggregated statistics. The work was funded by EU Horizon Europe, grant 101,095,095.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Universität Trier](https://md.research.elamur.ai/free/en/universities/universitat-trier/) — University, Trier, Germany · 8 works · 9 citations

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## Cite

Cite as: 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. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{munker2026don,
  title = {{Don't Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism}},
  author = {Simon Münker, Nils Schwager and Achim Rettinger},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.51},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.51}
}
```

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*Updated 31 October 2026*