ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems

TL;DR

ChestyBot is a language model designed to detect and disrupt Chinese Communist Party influence campaigns in real time, achieving high accuracy and offering a new framework for countering foreign information operations.

Contribution

The paper introduces ChestyBot, a pragmatics-based language model that effectively detects foreign influence tweets and provides a framework to disrupt influence operations early.

Findings

Detects influence tweets with up to 98.34% accuracy

Supports real-time identification of influence campaigns

Provides a novel disruption framework for influence operations

Abstract

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.