EuroVisShort2025

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https://diglib.eg.org/handle/10.2312/3607192

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Browsing EuroVisShort2025 by Subject "CCS Concepts: Human-centered computing → Empirical studies in visualization"

Now showing 1 - 2 of 2
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    Seeing Identity in Data: Can Anthropographics Uncover Racial Homophily in Emotional Responses?
    (The Eurographics Association, 2025) Sukumar, Poorna Talkad; Porfiri, Maurizio; Nov, Oded; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
    Racial homophily refers to the tendency of individuals to associate with others of the same racial or ethnic background. A recent study found no evidence of racial homophily in responses to mass shooting data visualizations. To increase the likelihood of detecting an effect, we redesigned the experiment by replacing bar charts with anthropographics and expanding the sample size. In a crowdsourced study (N=720), we showed participants a pictograph of mass shooting victims in the United States, with victims from one of three racial groups (Hispanic, Black, or White) highlighted. Each participant was assigned a visualization highlighting either their own racial group or a different racial group, allowing us to assess the influence of racial concordance on changes in affect (emotion). We found that, across all conditions, racial concordance had a modest but significant effect on changes in affect, with participants experiencing greater negative affect change when viewing visualizations highlighting their own race. This study provides initial evidence that racial homophily can emerge in responses to data visualizations, particularly when using anthropographics.
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    Transparent Risks Revisited: Evidence for a Dark-is-More Bias in Risk Perception
    (The Eurographics Association, 2025) Matzen, Laura E.; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
    Prior research has shown that different representations of uncertainty in data visualizations can lead to more (or less) riskaverse decision making. It is crucial for researchers to develop a better scientific understanding of these effects so that visualizations such as hazard maps can be designed to support viewers in reasoning about risk and probability. This paper presents a follow-up to a prior study that showed that participants underestimated the risk from a wildfire when transparency was used to represent different risk levels. In the present study, we test the hypothesis that the participants' decisions about risk are influenced by the dark-is-more bias. Across three experiments using the same wildfire evacuation task, we found that participants were consistently more likely to evacuate when the probability bands representing the fire risk were darker.