Missing the Bigger Picture: The Need for More Research on Visual Health Misinformation

Heley, Kathryn; Gaysynsky, Anna; King, Andy J.

doi:10.1177/10755470221113833

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…Revealing the contexts involved in producing data and visualizations is important to facilitate data reuse (Gregory et al, 2020), increase transparency (Kennedy et al, 2020), and counter misinformation (Heley et al, 2022). Such contexts can be hinted at using a reference to a data source or data set, although this is far from being a standardized practice in scholarly or science communication Zamith, 2019).…”

Section: Introductionmentioning

confidence: 99%

Data Journeys in Popular Science: Producing Climate Change and COVID-19 Data Visualizations at Scientific American

Gregory,

Koesten,

Schuster

et al. 2024

Harvard Data Science Review

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Vast amounts of (open) data are increasingly used to make arguments about crisis topics such as climate change and global pandemics. Data visualizations are central to bringing these viewpoints to broader publics.However, visualizations often conceal the many contexts involved in their production, ranging from decisions made in research labs about collecting and sharing data to choices made in editorial rooms about which data stories to tell. In this article, we examine how data visualizations about climate change and COVID-19 are produced in popular science magazines, using Scientific American, an established English-language popular science magazine, as a case study. To do this, we apply the analytical concept of 'data journeys' in a mixed methods study that centers on interviews with Scientific American staff and is supplemented by a visualization analysis of selected charts. In particular, we discuss the affordances of working with open data, the role of collaborative data practices, and how the magazine works to counter misinformation and increase transparency. This work provides an empirical contribution by providing insight into the data (visualization) practices of science communicators and demonstrating how the concept of data journeys can be used as an analytical framework.

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Section: Introductionmentioning

confidence: 99%

Data Journeys in Popular Science: Producing Climate Change and COVID-19 Data Visualizations at Scientific American

Gregory,

Koesten,

Schuster

et al. 2024

Harvard Data Science Review

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“…Given that misinformation continues to influence people's attitudes, beliefs, and judgments after correction, even debunked AI-generated images may result in long-term changes in belief. Proliferation of AI-generated visual misinformation may exacerbate existing public health issues (Heley et al, 2022), reduce trust in media (Karnouskos, 2020;Ternovski et al, 2022) and influence voter attitudes (Diakopoulos & Johnson, 2021;Dobber et al, 2021). Despite this threat, studies have yet to examine how people judge the veracity of AI-generated visual misinformation, and whether corrections can be effective for both immediate and long-term judgments.…”

Section: Introductionmentioning

confidence: 99%

Properties of AI-generated images that influence misinformation belief and correction efficacy

Guo,

Zhong,

2024

Preprint

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The rapid development of artificial intelligence (AI) allows the creation of photorealistic images with simple text prompts. As a result, proliferation of AI-generated visual misinformation is an increasing concern. However, how AI-generated visual misinformation influences our beliefs and whether corrections are effective remains to be seen. In study 1, we examined discernment between real headlines and headlines accompanied by AI-generated images (AI headlines), and how cognitive reflection, conspiracist beliefs and digital literacy correlated with belief in AI headlines. In study 2, after initial belief ratings of headlines, corrective information was provided, during which participants rated their surprise to a headline’s correction or confirmation. Beliefs were recorded immediately afterwards and after a 1-week delay. Subjective ratings of image realism, evidence and surprise were collected as predictors for initial belief and belief regression. Results showed that more realistic images and images that provided stronger evidence for the headline led to greater belief in both real and AI headlines, and that higher cognitive reflection ability and lower conspiracy beliefs were linked to improved discernment. Belief regression did not differ between real and AI headlines, and was primarily determined by how surprising the correction or confirmation was. Our findings suggest that people rely on image qualities such as its realism and how much evidence it provides to make judgements about headline veracity. Corrections were effective at reducing belief in AI headlines, thus emphasizing the importance of identifying and labeling AI content.

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“…While studies of textual dis-/misinformation are more common, there is rapidly growing appreciation of the necessity to do more research on the visual content of dis-/misinformation (see, e.g. the critical review on visual misinformation and the attempt at the systematization of the latter in [ 15 ]). As summarized by Heley et al [ 15 ], research shows visual information is, among other things, more persuasive and manipulative and more effective in prompting emotional responses.…”

Section: Introductionmentioning

confidence: 99%

“…the critical review on visual misinformation and the attempt at the systematization of the latter in [ 15 ]). As summarized by Heley et al [ 15 ], research shows visual information is, among other things, more persuasive and manipulative and more effective in prompting emotional responses. Visual information is remembered for longer and often affects people negatively in more covert ways, as visual manipulations tend to be easily overlooked.…”

Section: Introductionmentioning

confidence: 99%

“…Visual information is remembered for longer and often affects people negatively in more covert ways, as visual manipulations tend to be easily overlooked. In an attempt to answer the question of what visual misinformation is, Heley et al [ 15 ] demonstrate that visual content can either be misinformation in itself or constitute part of what we define as multimodal misinformation, e.g. when visual content is contextualized in a certain manner by the accompanying text or speech to become misinformation.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal analysis of disinformation and misinformation

Wilson,

Wilkes,

Teramoto

et al. 2023

R. Soc. open sci.

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The use of disinformation and misinformation campaigns in the media has attracted much attention from academics and policy-makers. Multimodal analysis or the analysis of two or more semiotic systems—language, gestures, images, sounds, among others—in their interrelation and interaction is essential to understanding dis-/misinformation efforts because most human communication goes beyond just words. There is a confluence of many disciplines (e.g. computer science, linguistics, political science, communication studies) that are developing methods and analytical models of multimodal communication. This literature review brings research strands from these disciplines together, providing a map of the multi- and interdisciplinary landscape for multimodal analysis of dis-/misinformation. It records the substantial growth starting from the second quarter of 2020—the start of the COVID-19 epidemic in Western Europe—in the number of studies on multimodal dis-/misinformation coming from the field of computer science. The review examines that category of studies in more detail. Finally, the review identifies gaps in multimodal research on dis-/misinformation and suggests ways to bridge these gaps including future cross-disciplinary research directions. Our review provides scholars from different disciplines working on dis-/misinformation with a much needed bird's-eye view of the rapidly emerging research of multimodal dis-/misinformation.

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Missing the Bigger Picture: The Need for More Research on Visual Health Misinformation

Cited by 11 publications

References 22 publications

Data Journeys in Popular Science: Producing Climate Change and COVID-19 Data Visualizations at Scientific American

Data Journeys in Popular Science: Producing Climate Change and COVID-19 Data Visualizations at Scientific American

Properties of AI-generated images that influence misinformation belief and correction efficacy

Multimodal analysis of disinformation and misinformation

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