Adversarial Creation and Detection of AI-Generated Social Bot Content

  1. Mykola Trokhymovych, Ricardo Baeza-Yates, Alessandro Flammini, Diego Saez-Trumper, Filippo Menczer
    Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM ’26), 2026
    arXiv PDF Code

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.

Citation

@inproceedings{trokhymovych2026adversarial,
  title = {Adversarial Creation and Detection of {AI}-Generated Social Bot Content},
  author = {Trokhymovych, Mykola and Baeza-Yates, Ricardo and Flammini, Alessandro and Saez-Trumper, Diego and Menczer, Filippo},
  booktitle = {Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM '26)},
  year = {2026},
  url = {https://arxiv.org/abs/2606.07219},
}

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