ERIC Number: EJ1461027
Record Type: Journal
Publication Date: 2025-Dec
Pages: 27
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2365-7464
Available Date: 2025-02-21
Narrative Visualizations: Depicting Accumulating Risks and Increasing Trust in Data
Madison Fansher6; Logan Walls1; Chenxu Hao2; Hari Subramonyam3; Aysecan Boduroglu4; Priti Shah1; Jessica K. Witt5
Cognitive Research: Principles and Implications, v10 Article 7 2025
In contexts where people lack prior knowledge and risk awareness--such as the COVID-19 pandemic--even truthful visualizations of data can seem surprising. This can lead people to mistrust the veracity of the data and to discount it, leading to poor risk decisions. In this work, we illustrate how narrative visualizations can achieve a balance between the benefits of three common risk communication mediums (static visualizations, interactive simulations, and affect-laden anecdotes). We demonstrate empirically that viewing a narrative visualization mitigates the reduced concern induced by a static visualization when communicating COVID-19 transmission risk (Study 1). Through mediation analysis, we show that narrative visualizations are more effective than static visualizations at increasing concern about large risks because they increase one's perceived understanding and trust in data (Study 2). We argue that narrative visualizations deserve attention as a distinct class of visualizations that have the potential to be powerful tools for scientific communication (especially in contexts where data are surprising, and empiricism is important).
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://bibliotheek.ehb.be:2123/
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: 2030059
Data File: URL: https://osf.io/k43ev/?view_only=67cc6401b06946889ac499fc19afec2f
Author Affiliations: 1University of Michigan, Department of Psychology, Ann Arbor, USA; 2Delft University of Technology, Department of Intelligent Systems, Delft, The Netherlands; 3Stanford University, Graduate School of Education, Stanford, USA; 4KoƧ University, Department of Psychology, Istanbul, Turkey; 5Colorado State University, Fort Collins, USA; 6University of Michigan, Department of Physical Medicine & Rehabilitation, Ann Arbor, USA