The study aims to test whether simple priming of deepfake (DF) information significantly increases users' ability to recognize DF media. Although undoubtedly fascinating from a technological point of view, these highly realistic artificial intelligent (AI)-generated fake videos hold high deceptive potential. Both practitioners and institutions are thus joining forces to develop debunking strategies to counter the spread of such difficult-to-recognize and potentially misleading video content. On this premise, this study addresses the following research questions: does simple priming with the definition of DFs and information about their potentially harmful applications increase users' ability to recognize DFs? Does bullshit receptivity, as an individual tendency to be overly accepting of epistemically suspect beliefs, moderate the relationship between such priming and DF recognition? Results indicate that the development of strategies to counter the deceitfulness of DFs from an educational and cultural perspective might work well, but only for people with a lower susceptibility to believe willfully misleading claims. Finally, through a serial mediation analysis, we show that DF recognition does, in turn, negatively impact users' sharing intention, thus limiting the potential harm of DFs at the very root of one of their strengths: virality. We discuss the implications of our finding that society's defense against DFs could benefit from a simple reasoned digital literacy intervention.

中文翻译：

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揭露的Deepfake：信息启动和废话接受度对Deepfake识别和共享意图的影响

这项研究旨在测试是否可以通过简单地添加Deepfake（DF）信息来显着提高用户识别DF媒体的能力。尽管从技术角度来看无疑令人着迷，但这些高度逼真的人工智能（AI）生成的假视频具有很高的欺骗潜力。因此，从业者和机构都在联合起来，共同制定揭穿策略，以应对这种难以识别且可能引起误解的视频内容的传播。在此前提下，本研究解决了以下研究问题：使用DF的定义进行简单启动以及有关其潜在有害应用的信息是否会提高用户识别DF的能力？废话接受性是个人倾向于过度接受认识论上可疑的信念的一种倾向吗，缓和这种启动和DF识别之间的关系？结果表明，从教育和文化角度出发，制定应对DF欺诈行为的策略可能会奏效，但仅适用于那些不愿相信故意误导性主张的人。最后，通过一系列的中介分析，我们发现DF识别确实反过来对用户的共享意图产生负面影响，从而从其优势之一的根源上限制了DF的潜在危害：病毒性。我们讨论了我们的发现的含义，即社会对DF的防御可以从简单的合理数字素养干预中受益。