Narrative visualizations
Depicting accumulating risks and increasing trust in data
Madison Fansher (University of Michigan)
Logan Walls (University of Michigan)
C. Hao (TU Delft - Pattern Recognition and Bioinformatics)
Hari Subramonyam (Stanford University)
Aysecan Boduroglu (Koç University)
Priti Shah (University of Michigan)
Jessica K. Witt (Colorado State University)
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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).
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