What Is So Deep About Deepfakes? A Multi-Disciplinary Thematic Analysis of Academic Narratives About Deepfake Technology

John Twomey, Didier Ching, Matthew Peter Aylett, Michael Quayle, Conor Linehan, Gillian Murphy

Research output: Contribution to journalArticlepeer-review

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Abstract

Deepfakes are a form of synthetic media that uses deep-learning technology to create fake images, video, and audio. The emergence of this technology has inspired much commentary and speculation from academics across a range of disciplines, who have contributed expert opinions regarding the implications of deepfake proliferation on fields such as law, politics, and entertainment. A systematic scoping review was carried out to identify, assemble, and critically analyze those academic narratives. The aim is to build on and critique previous attempts at defining the technology and categorizing the harms and benefits of deepfake technology. A range of databases were searched for relevant articles from 2017 to 2023, resulting in a large multi-disciplinary dataset of 102 papers, 181,659 words long, which were analyzed qualitatively through thematic analysis. Implications for future research include questioning the lack of research evidence for the supposed positives of deepfakes, recognizing the role that identity plays in deepfake technology, challenging the perceived accessibility/ believability of deepfakes, and proposing a more nuanced approach to the dichotomous “positive and negatives” of deepfakes. Furthermore, we show how definitional issues around what a deepfake is versus other forms of fake media feeds confusion around the novelty and impacts of deepfakes.
Original languageEnglish
Pages (from-to)64-79
Number of pages16
JournalIEEE Transactions on Technology and Society
Volume6
Issue number1
Early online date18 Nov 2024
DOIs
Publication statusPublished - Mar 2025

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