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深度伪造技术的公众心理表征:通过Quora文本数据分析进行的深入定性探索。

The public mental representations of deepfake technology: An in-depth qualitative exploration through Quora text data analysis.

作者信息

Caci Barbara, Giordano Giulia, Alesi Marianna, Gentile Ambra, Agnello Chiara, Lo Presti Liliana, La Cascia Marco, Ingoglia Sonia, Inguglia Cristiano, Volpes Alice, Monzani Dario

机构信息

Department of Psychology, Educational Sciences and Human Movement, University of Palermo, Palermo, Italy.

Department of Engineering, University of Palermo, Palermo, Italy.

出版信息

PLoS One. 2024 Dec 30;19(12):e0313605. doi: 10.1371/journal.pone.0313605. eCollection 2024.

DOI:10.1371/journal.pone.0313605
PMID:39775334
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11684586/
Abstract

The advent of deepfake technology has raised significant concerns regarding its impact on individuals' cognitive processes and beliefs, considering the pervasive relationships between technology and human cognition. This study delves into the psychological literature surrounding deepfakes, focusing on people's public representation of this emerging technology and highlighting prevailing themes, opinions, and emotions. Under the media framing, the theoretical framework is crucial in shaping individuals' cognitive schemas regarding technology. A qualitative method has been applied to unveil patterns, correlations, and recurring themes of beliefs about the main topic, deepfake, discussed on the forum Quora. The final extracted text corpus consisted of 166 answers to 17 questions. Analysis results highlighted the 20 most prevalent critical lemmas, and deepfake was the main one. Moreover, co-occurrence analysis identified words frequently appearing with the lemma deepfake, including video, create, and artificial intelligence-finally, thematic analysis identified eight main themes within the deepfake corpus. Cognitive processes rely on critical thinking skills in detecting anomalies in fake videos or discerning between the negative and positive impacts of deepfakes from an ethical point of view. Moreover, people adapt their beliefs and mental schemas concerning the representation of technology. Future studies should explore the role of media literacy in helping individuals to identify deepfake content since people may not be familiar with the concept of deepfakes or may not fully understand the negative or positive implications. Increased awareness and understanding of technology can empower individuals to evaluate critically the media related to Artificial Intelligence.

摘要

考虑到技术与人类认知之间普遍存在的关系,深度伪造技术的出现引发了人们对其对个人认知过程和信念影响的重大担忧。本研究深入探讨了围绕深度伪造的心理学文献,重点关注人们对这项新兴技术的公开表述,并突出了普遍存在的主题、观点和情感。在媒体框架下,理论框架对于塑造个人对技术的认知模式至关重要。本研究采用定性方法,揭示了在问答网站Quora上讨论的关于主要主题“深度伪造”的信念模式、相关性和反复出现的主题。最终提取的文本语料库由针对17个问题的166个答案组成。分析结果突出了20个最普遍的关键引理,其中“深度伪造”是主要的一个。此外,共现分析确定了经常与引理“深度伪造”一起出现的词,包括视频、创建和人工智能。最后,主题分析确定了深度伪造语料库中的八个主要主题。认知过程依赖于批判性思维技能,以检测假视频中的异常情况,或从伦理角度辨别深度伪造的负面影响和正面影响。此外,人们会调整他们关于技术表现的信念和心理模式。未来的研究应该探索媒体素养在帮助个人识别深度伪造内容方面的作用,因为人们可能不熟悉深度伪造的概念,或者可能没有完全理解其负面或正面影响。提高对技术的认识和理解可以使个人有能力批判性地评估与人工智能相关的媒体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50dd/11684586/8387637a8559/pone.0313605.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50dd/11684586/589ec23bc26f/pone.0313605.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50dd/11684586/8387637a8559/pone.0313605.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50dd/11684586/589ec23bc26f/pone.0313605.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50dd/11684586/8387637a8559/pone.0313605.g002.jpg

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本文引用的文献

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Deepfakes and scientific knowledge dissemination.深度伪造与科学知识传播。
Sci Rep. 2023 Aug 18;13(1):13429. doi: 10.1038/s41598-023-39944-3.
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Face/Off: Changing the face of movies with deepfakes.变脸:用深度伪造技术改变电影面貌。
PLoS One. 2023 Jul 6;18(7):e0287503. doi: 10.1371/journal.pone.0287503. eCollection 2023.
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Fooled twice: People cannot detect deepfakes but think they can.被愚弄两次:人们无法察觉深度伪造,但却认为自己能做到。
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Fighting Deepfakes by Detecting GAN DCT Anomalies.通过检测生成对抗网络离散余弦变换异常来对抗深度伪造
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The attention schema theory in a neural network agent: Controlling visuospatial attention using a descriptive model of attention.神经网络代理中的注意模式理论:使用注意描述模型控制视空间注意。
Proc Natl Acad Sci U S A. 2021 Aug 17;118(33). doi: 10.1073/pnas.2102421118.
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The Social Impact of Deepfakes.深度伪造技术的社会影响。
Cyberpsychol Behav Soc Netw. 2021 Mar;24(3):149-152. doi: 10.1089/cyber.2021.29208.jth.
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Effects of Disinformation Using Deepfake: The Protective Effect of Media Literacy Education.利用深度伪造信息的影响:媒体素养教育的保护作用。
Cyberpsychol Behav Soc Netw. 2021 Mar;24(3):188-193. doi: 10.1089/cyber.2020.0174. Epub 2021 Mar 1.
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Deep Fakes and Memory Malleability: False Memories in the Service of Fake News.深度伪造与记忆可塑性:服务于假新闻的虚假记忆
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