Effects of Disinformation Using Deepfake: The Protective Effect of Media Literacy Education
Abstract
This research examines (a) the negative impact of disinformation including a deepfake video and (b) the protective effect of media literacy education. We conducted an experiment using a two disinformation message type (deepfake video present vs. absent) by three media literacy education (general disinformation vs. deepfake-specific vs. no literacy) factorial design. In the general disinformation (vs. deepfake-specific) literacy condition, participants were informed about (a) the definition of disinformation (vs. deepfake), (b) some examples of disinformation (vs. deepfake), and (c) the social consequences of disinformation (vs. deepfake). Results showed that disinformation messages including a deepfake video resulted in greater vividness, persuasiveness, credibility, and intent to share the message. Media literacy education reduced the effects of disinformation messages.
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A consideration of the reputation of the information source, the involvement of trustworthy intermediaries such as experts and/or opinion leaders, and personal confidence based on first-hand experiences will further enhance consumers' protection.
“A consideration of the reputation of the information source, the involvement of trustworthy intermediaries such as experts and/or opinion leaders, and personal confidence based on first-hand experiences will further enhance their protection (Hwang et al., 2021; Viviani & Pasi, 2017; Westerlund, 2019; Whittaker et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Hwang et al., 2021
The problem is not only that deepfake technology is improving at a very fast pace; it is that the social processes through which we collectively acquire knowledge and determine whether something is genuine or deceptive are under threat and that the very definition of reality is a critical concern.
“The problem is not only that deepfake technology is improving at a very fast pace (Johnson & Diakopoulos, 2021; Schwartz, 2018); it is that the social processes through which we collectively acquire knowledge and determine whether something is genuine or deceptive are under threat and that the very definition of reality is a critical concern (Hwang et al., 2021; Schwartz, 2018).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Hwang et al., 2021
Hwang et al. (2021) found that a fake news article with a deepfake was perceived as more persuasive, more vivid, more credible, and led to higher sharing intentions than the same article without the deepfake.
“Results suggest that the fake news article with deepfake was perceived to be more persuasive, more vivid, more credible, and led to higher sharing intentions than the fake news article without the deepfake.”
From The detection of political deepfakes (2022) · cites this paper as Hwang et al., 2021
Prior studies are in line with the assumption that deepfakes shape recipients' thoughts and behavior and that deepfakes may warrant information in a fake news article.
“They are further in line with the assumption that deepfakes shape recipients' thoughts and behavior (Dobber et al., 2021;Hwang et al., 2021) and that deepfakes may warrant information in a fake news article (Hwang et al., 2021).”
From The detection of political deepfakes (2022) · cites this paper as Hwang et al., 2021
Results on the effectiveness of treatments meant to foster critical scrutiny were mixed.
“Results on the effectiveness of treatments meant to foster critical scrutiny were mixed (Hwang et al., 2021;Ko ¨bis et al., 2021).”
From The detection of political deepfakes (2022) · cites this paper as Hwang et al., 2021
One available empirical study demonstrated that forewarnings are able to combat the influence of deepfakes (Hwang et al. 2021).
“one available empirical study demonstrated that forewarnings are able to combat the influence of deepfakes (Hwang et al. 2021)”
From The detection of political deepfakes (2022) · cites this paper as Hwang et al. 2021
In Study 3, participants were randomly assigned to read a fake news story supported by a deepfake or the same fake news story without the deepfake.
“In Study 3, we randomly assigned participants to read a fake news story that was supported by a deepfake or to read the same fake news story without the deepfake (see also Hwang et al., 2021).”
From The detection of political deepfakes (2022) · cites this paper as Hwang et al., 2021
The lack of an effect of including a deepfake to fake news contrasts with our assumptions and the results of a study by Hwang et al. (2021).
“The lack of an effect of including a deepfake to fake news contrasts with our assumptions and the results of a study by Hwang et al. (2021).”
From The detection of political deepfakes (2022) · cites this paper as Hwang et al., 2021
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- Authors
- Hwang, Y., Ryu, J. Y., & Jeong, S.-H.
- Affiliations
- Myongji University (KR)
- Korea University (KR)
- Corresponding author
- Jeong, Se-Hoon
- Venue
- Cyberpsychology, Behavior, and Social Networking
- Publisher
- SAGE Publications
- Published
- 2021-03-01
- Volume
- 24
- Issue
- 3
- Pages
- 188-193
- ISSN
- 2152-2715 (ISSN-L)
- 2152-2723
- OpenAlex
- W3135235202
- Cited by
- 148
- References
- 45
- Citations by year
- 2026: 27 · 2025: 40 · 2024: 37 · 2023: 24 · 2022: 15 · 2021: 5
- FWCI
- 28.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cyberpsychology Behavior and Social Networking (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Literacy, Media, and Education
- Social Sciences › Sociology and Political Science › Islamic Finance and Communication
- Keywords
- disinformation, deepfake videos, media literacy education, message credibility, message persuasiveness, sharing intention
- MeSH terms
- Communication, Female, Humans, Deception, Male, Persuasive Communication, Video Recording, Intention, Consumer Health Information, Young Adult, Health Literacy, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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