Beyond (Mis)Representation: Visuals in COVID-19 Misinformation
Open accessAbstract
This article provides one of the first analyses of visuals in misinformation concerning COVID-19. A mixed-methods analysis of ninety-six examples of visuals in misinformation rated false or misleading by independent professional fact-checkers from the first three months of 2020 identifies and examines six frames and three distinct functions of visuals in pieces of misinformation: how visuals illustrate and selectively emphasize arguments and claims, purport to present evidence for claims, and impersonate supposedly authoritative sources for claims. Notably, visuals in more than half of the pieces of misinformation analyzed explicitly serve as evidence for false claims, most of which are mislabelled rather than manipulated. While this analysis uncovered a small number of manipulated visuals, all were produced using simple tools; there were no examples of “deepfakes” or other artificial intelligence-based techniques. In recognizing the diverse functions of visuals in misinformation and drawing on recent literature on scientific visualization, this article demonstrates the value in both attending to visual content in misinformation and expanding our focus beyond a concern with only the representational aspects and functions of misinformation.
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Most examples of visual disinformation and information warfare today are based on deceptive re-contextualization or low-effort editing of visual materials.
“Hence, although concerns on AI-powered disinformation are voiced loudly in the public domain, most examples of visual disinformation and information warfare today are based on the deceptive re-contextualization or low-effort editing of visual materials.”
From How persuasive are political cheapfakes disseminated via social media? The effects of out-of-context visual disinformation on message credibility and issue agreement (2025) · cites this paper as Brennen et al., 2021
The Findings (Serving as Evidence; page not provided) report that 35% of the sampled COVID-19 misinformation used misleading visual context and about 10% used manipulation, all involving simple editing. The Discussion (page not provided) reports no AI-based manipulations in the sample. These findings support the contrast with AI-powered content, but not the broader claim that most visual disinformation and information warfare today use those methods: the study concerns COVID-19 misinformation, and the two categories together account for less than half its sample. (LLM, conf=0.88)
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References 40 · Cited by 179
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- Authors
- Brennen, J. S., Simon, F. M., & Nielsen, R. K.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Brennen, J. Scott
- Venue
- The International Journal of Press/Politics
- Publisher
- SAGE Publications
- Published
- 2020-10-10
- Volume
- 26
- Issue
- 1
- Pages
- 277-299
- ISSN
- 1040-1620 (ISSN-L)
- 1940-1612
- 1940-1620
- PMC
- PMC7548543
- OpenAlex
- W3092042428
- Cited by
- 179
- References
- 40
- Citations by year
- 2026: 22 · 2025: 31 · 2024: 29 · 2023: 36 · 2022: 40 · 2021: 19 · 2020: 2
- FWCI
- 32.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The International Journal of Press/Politics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Oxford Martin School, University of Oxford (Oxford Martin Programme on Misinformation, Science)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- COVID-19 misinformation, visual misinformation, manipulated images, image editing, visual framing, visual evidence, scientific visualization, fact-checking, deepfakes
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