用语言模型识别中文影响力攻击，准确率超98%
ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems
- 基于语用学构建语言模型，分析潜在恶意推文
- 对未标记推文的检测准确率达98.34%
- 可提前干预境外信息操纵，适合安全与舆情研究者
俄罗斯和中国等外国行为体利用美国开放的信息环境开展信息行动，威胁民主制度与威斯特伐利亚体系。然而现有检测与应对策略常无法实时发现此类活动。本文提出ChestyBot，一种基于语用学的语言模型，可对未标记的外国恶意影响推文实现最高98.34%的检测准确率。该模型支持一种新框架，可在境外影响力操作的形成初期即进行干扰，有效遏制其扩散。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。