Foreign information operations conducted by Russian and Chinese actors exploit the United States' permissive information environment. These campaigns threaten democratic institutions and the broader Westphalian model. Yet, existing detection and mitigation strategies often fail to identify active information campaigns in real time. This paper introduces ChestyBot, a pragmatics-based language model that detects unlabeled foreign malign influence tweets with up to 98.34% accuracy. The model supports a novel framework to disrupt foreign influence operations in their formative stages.
@article{arxiv.2505.10746,
title = {ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems},
author = {Matthew Stoffolano and Ayush Rout and Justin M. Pelletier},
journal= {arXiv preprint arXiv:2505.10746},
year = {2025}
}
Comments
Presented at USCYBERCOM Cyber Recon Symposium 2023 at DreamPort in Columbia, MD on April 20, 2023