Dash Saloni, Xu Yiwei, Jalbert Madeline, Spiro Emma S
Information School, University of Washington, 1851 NE Grant L, Seattle, WA 98105, USA.
PNAS Nexus. 2025 Jul 22;4(7):pgaf207. doi: 10.1093/pnasnexus/pgaf207. eCollection 2025 Jul.
In this article, we study how AI-paraphrased messages have the potential to amplify the persuasive impact and scale of information campaigns. Building from social and cognitive theories on repetition and information processing, we model how -a common repetition tactic leveraged by information campaigns-can be enhanced using large language models. We first extract CopyPasta from two prominent disinformation campaigns in the United States and use ChatGPT to paraphrase the original message to generate . We then validate that AIPasta is lexically diverse in comparison to CopyPasta while retaining the semantics of the original message using natural language processing metrics. In a preregistered experiment comparing the persuasive potential of CopyPasta and AIPasta ( = 1,200), we find that AIPasta (but not CopyPasta) is effective at increasing perceptions of consensus in the broad false narrative of the campaign while maintaining similar levels of sharing intent with respect to Control (CopyPasta reduces such intent). Additionally, AIPasta (vs. Control) increases belief in the exact false claim of the campaign, depending on political orientation. However, across most outcomes, we find little evidence of significant persuasive differences between AIPasta and CopyPasta. Nonetheless, current state-of-the-art AI-text detectors fail to detect AIPasta, opening the door for these operations to scale successfully. As AI-enabled information operations become more prominent, we anticipate a shift from traditional CopyPasta to AIPasta, which presents significant challenges for detection and mitigation.
在本文中，我们研究了人工智能改写的信息如何有可能扩大信息传播活动的说服力和规模。基于关于重复和信息处理的社会及认知理论，我们建立了一个模型，以说明信息传播活动常用的重复策略如何利用大语言模型得到强化。我们首先从美国的两场著名虚假信息传播活动中提取复制粘贴内容，并使用ChatGPT对原始信息进行改写以生成人工智能改写内容。然后，我们使用自然语言处理指标验证，与复制粘贴内容相比，人工智能改写内容在词汇上具有多样性，同时保留了原始信息的语义。在一项预先注册的实验中，我们比较了复制粘贴内容和人工智能改写内容（样本量 = 1200）的说服潜力，发现人工智能改写内容（而非复制粘贴内容）在增加对传播活动广泛虚假叙述中的共识认知方面是有效的，同时在分享意愿方面与对照组保持相似水平（复制粘贴内容降低了这种意愿）。此外，根据政治倾向，人工智能改写内容（与对照组相比）增加了对传播活动确切虚假主张的相信程度。然而，在大多数结果中，我们几乎没有发现人工智能改写内容和复制粘贴内容之间存在显著说服差异的证据。尽管如此，当前最先进的人工智能文本检测器无法检测到人工智能改写内容，这为这些操作的成功扩展打开了大门。随着人工智能驱动的信息操作变得更加突出，我们预计将从传统的复制粘贴内容转向人工智能改写内容，这给检测和缓解带来了重大挑战。
