Adversarial Creation and Detection of AI-Generated Social Bot Content
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Computer Science > Computation and Language
Title:Adversarial Creation and Detection of AI-Generated Social Bot Content
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: | arXiv:2606.07219 [cs.CL] |
| (or arXiv:2606.07219v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07219
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Mykola Trokhymovych [view email][v1] Fri, 5 Jun 2026 12:32:47 UTC (1,155 KB)
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