An Agenda for Studying Credibility Perceptions of Visual Misinformation

ABSTRACT

Today’s political misinformation has increasingly been created and consumed in visual formats, such as photographs, memes, and videos. Despite the ubiquity of visual media and the growing scholarly attention to misinformation, there is a relative dearth of research on visual misinformation. It remains unclear which specific visual formats (e.g., memes, visualizations) and features (e.g., color, human faces) contribute to visual misinformation's influence, either on their own or in combination with non-visual features and heuristics, and through what mechanisms. In response to these gaps, we identify a theoretical framework that explains the persuasive mechanisms and pathways of visual features in lending credibility (e.g., as arguments, heuristics, and attention determinants). We propose a list of relevant visual attributes to credibility perceptions and a research agenda that integrates methods including computational visual analysis, crowdsourced annotations, and experiments to advance our understanding of visual misinformation.

KEYWORDS:

• Visual misinformation
• misinformation
• credibility
• image
• video

Disclosure Statement

All three authors declare that we do not have any relevant financial or non-financial competing interests.

Supplementary Material

Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/10584609.2023.2175398

Additional information

Funding

This work was supported by the National Science Foundation [CNS-2150716, CNS-2150723]; Stanford Program on Democracy and the Internet (PDI) Research Funding, Stanford University.

Notes on contributors

Yilang Peng

Yilang Peng (PhD, Annenberg School for Communication, University of Pennsylvania) is an Assistant Professor in the Department of Financial Planning, Housing and Consumer Economics at the University of Georgia. His research areas include computational social science, visual communication, and science communication (with a focus on public perceptions of artificial intelligence).

Yingdan Lu

Yingdan Lu is a PhD candidate in the Department of Communication at Stanford University. Her research applies data analytic and computational methods to large-scale multimodal datasets to explore how authoritarian governments use digital media to maintain their rule and how individuals experience digital technology in different media environments. For more information, see her website: https://yingdanlu.com/.

Cuihua Shen

Cuihua Shen (PhD, University of Southern California) is a Professor of Communication at the University of California, Davis. Her research interests include online networks, misinformation, and computational social science.