人工智能简述的信息在规模上的说服力潜力
Saloni Dash1, Yiwei Xu1, Madeline Jalbert1
1Information School, University of Washington, 1851 NE Grant L, Seattle, WA 98105, USA.
PNAS nexus
|July 23, 2025
概括
人工智能转述的信息,或AIPasta,可以通过增加知觉的共识来放大虚假信息运动. 虽然在说服方面与传统的CopyPasta相似,但AIPasta避免了当前的AI检测,这给缓解带来了重大挑战.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 错误信息研究 错误信息研究
背景情况:
- 信息宣传活动经常使用消息重复 (CopyPasta) 来说服受众.
- 大型语言模型 (LLM) 为生成多样化但语义上一致的文本提供了新的功能.
研究的目的:
- 调查LLM如何在信息宣传活动中增强消息重复策略.
- 为了比较人工智能转述的信息 (AIPasta) 与传统的CopyPasta的说服力影响和检测能力.
主要方法:
- 从美国的虚假信息活动中提取了CopyPasta.
- 使用ChatGPT生成AIPasta来对原始消息进行转述.
- 使用NLP指标验证了AIPasta的词汇多样性和语义一致性.
- 进行了一项预先注册的实验 (N=1,200),比较了CopyPasta,AIPasta和Control的说服力.
主要成果:
- 与CopyPasta不同的是,AIPasta增加了对共识的看法和对虚假声明的信念 (根据政治取向而异),不像CopyPasta.
- 与控制相比,AIPasta保持了共享意图,而CopyPasta则减少了共享意图.
- 在大多数结果中,在AIPasta和CopyPasta之间没有发现显著的说服力差异.
- 目前的AI文本检测器无法识别AIPasta.
结论:
- 通过增强说服力影响和逃避检测,AIPasta可以有效地扩展信息操作.
- 该研究强调了在虚假信息运动中从CopyPasta转向AIPasta的潜在转变.
- 在检测和减轻人工智能驱动的信息操作方面存在重大挑战.
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