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arXiv · 2606.07219

Adversarial Creation and Detection of AI-Generated Social Bot Content

Mykola Trokhymovych·Ricardo Baeza-Yates·Alessandro Flammini·Diego Saez-Trumper·Filippo Menczer
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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.

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Mykola Trokhymovych, Ricardo Baeza-Yates, Alessandro Flammini, Diego Saez-Trumper, Filippo Menczer. 2026-06-05. Adversarial Creation and Detection of AI-Generated Social Bot Content. https://arxiv.org/abs/2606.07219

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