"He looks very real": Media, knowledge, and search‐based strategies for deepfake identification.
Deepfakes are a potential source of disinformation and the ability to detect them is imperative. While research focused on algorithmic detection methods, there is little work conducted on how people identify deepfakes. This research attempts to fill this gap. Using semi‐structured interviews, partic...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 75; no. 6; pp. 643 - 655 |
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| Autor principal: | |
| Formato: | research tables/charts Journal Article |
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Wiley-Blackwell
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177189404&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177189404 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: Jun2024 vid: 75 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 177189404 174594923 177189404 177189404 10.1002/asi.24867 177189404 ppf: 643 ppct: 12 formats: tig: atl: "He looks very real": Media, knowledge, and search‐based strategies for deepfake identification. aug: au: Goh, Dion Hoe‐Lian affil: Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore sug: subj: Deep Learning Methods Misinformation Evaluation Videorecording Standards Internet Searching Methods Social Media Knowledge Models, Theoretical Human Algorithms Semi-Structured Interview Teaching Materials Data Analysis Software Male Female Adult Funding Source Communication Adult: 19-44 years Male Female ab: Deepfakes are a potential source of disinformation and the ability to detect them is imperative. While research focused on algorithmic detection methods, there is little work conducted on how people identify deepfakes. This research attempts to fill this gap. Using semi‐structured interviews, participants were asked to identify real and deepfake videos and explain how their decisions were made. Three categories of deepfake identification strategies emerged: the use of surface video and audio cues, processing of the messages conveyed in the video, and the searching of external sources. Participants often used multiple strategies within each category. However, identification challenges occurred due to participants' preconceived notions of deepfake characteristics and the message embodied in the video. This work contributes to research by shifting the focus from the algorithmic detection of deepfakes to human‐oriented strategies. Practically, the findings provide guidance on how people can identify deepfakes, which can also form the basis for the development of educational materials. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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