ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 38 references that are in the map. First posted on arXiv 15 May 2025.
Builds on
7 of 38 references.
References · 7
- 2015Recent Advances in Convolutional Neural Networkscited by 360
- 2021BERT: A Review of Applications in Natural Language Processing and Understandingcited by 109
- 2020Pooling Methods in Deep Neural Networks, a Reviewcited by 52
- 2020Uncovering Coordinated Networks on Social Media: Methods and Case Studiescited by 49
- 2020On the Detection of Disinformation Campaign Activity with Network Analysiscited by 15
- 2018Characterizing the 2016 Russian IRA Influence Campaigncited by 11
- 2020Automatic Detection of Influential Actors in Disinformation Networkscited by 8
Built on by
Not cited yet.