ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems

Fuente: arXiv
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Auteurs principaux: Stoffolano, Matthew, Rout, Ayush, Pelletier, Justin M.
Format: Preprint
Publié: 2025
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author Stoffolano, Matthew
Rout, Ayush
Pelletier, Justin M.
author_facet Stoffolano, Matthew
Rout, Ayush
Pelletier, Justin M.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_10746
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems
Stoffolano, Matthew
Rout, Ayush
Pelletier, Justin M.
Computers and Society
Artificial Intelligence
Cryptography and Security
Social and Information Networks
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.
title ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems
topic Computers and Society
Artificial Intelligence
Cryptography and Security
Social and Information Networks
url https://arxiv.org/abs/2505.10746