[2606.07219] Adversarial Creation and Detection of AI-Generated Social Bot Content

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Computer Science > Computation and Language

arXiv:2606.07219 (cs)

[Submitted on 5 Jun 2026]

Title:Adversarial Creation and Detection of AI-Generated Social Bot Content

Authors:Mykola Trokhymovych, Ricardo Baeza-Yates, Alessandro Flammini, Diego Saez-Trumper, Filippo Menczer<br>View a PDF of the paper titled Adversarial Creation and Detection of AI-Generated Social Bot Content, by Mykola Trokhymovych and 4 other authors

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Abstract:The convergence of large language models and social bots allows malicious actors to manipulate the information ecosystem by generating human-like content at scale. Existing models for detecting AI-generated content often fail in the wild, primarily due to the lack of ground-truth data. We address this gap through an adversarial methodology that models the impersonation of real social media users by malicious actors. Using this methodology, we curate a multilingual, cross-platform dataset of paired human and AI-generated messages. Training on such adversarial data yields accurate detection of AI-generated text. Our approach significantly outperforms existing models for content-based bot detection in real-world, out-of-distribution data.

Subjects:

Computation and Language (cs.CL); Social and Information Networks (cs.SI)

Cite as:<br>arXiv:2606.07219 [cs.CL]

(or<br>arXiv:2606.07219v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.07219

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arXiv-issued DOI via DataCite

Submission history<br>From: Mykola Trokhymovych [view email]<br>[v1]<br>Fri, 5 Jun 2026 12:32:47 UTC (1,155 KB)

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