Publication: Narrative visualizations: depicting accumulating risks and increasing trust in data
Program
KU-Authors
KU Authors
Co-Authors
Fansher, Madison
Walls, Logan
Hao, Chenxu
Subramonyam, Hari
Shah, Priti
Witt, Jessica K.
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No
Journal Title
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Volume Title
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Abstract
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).
Source
Publisher
SPRINGER
Subject
Psychology
Citation
Has Part
Source
Cognitive Research-Principles and Implications
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Edition
DOI
10.1186/s41235-025-00613-w
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CC BY (Attribution)
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Creative Commons license
Except where otherwised noted, this item's license is described as CC BY (Attribution)

