# Adversarial Creation and Detection of AI-Generated Social Bot Content

- **Authors:** Mykola Trokhymovych, Ricardo Baeza-Yates, Alessandro Flammini, Diego Saez-Trumper, Filippo Menczer
- **Venue:** Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM ’26) (CIKM’26), 2026
- **Keywords:** social bots, AI-generated text detection, large language models, adversarial data, misinformation, multilingual NLP
- **Page:** https://www.trokhymovych.com/publications/adversarial-creation-and-detection-of-ai-generated-social-bot-content-2026/

## 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.

## Links

- arXiv: https://arxiv.org/abs/2606.07219
- PDF: https://arxiv.org/pdf/2606.07219
- Code: https://github.com/trokhymovych/ai-bot-detection

## Citation

```bibtex
@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},
}
```