IEEE VIS 2024 Content: Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

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 Carter Blair - University of Waterloo, Waterloo, Canada. University of Victoria, Victoria, Canada

 Xiyao Wang - University of Victoria, Victoira, Canada. Delft University of Technology, Delft, Netherlands

Charles Perin - University of Victoria, Victoria, Canada

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2024-10-16T16:24:00Z

Exemplar figure, described by caption below — We quantify through five studies the effects of color (Study 1 and Study 2), chart type (Study 3, Study 4, and Study 5), data trend (Study 2 and Study 3), data variance (Study 4), and data density (Study 5) on emotion (measured through arousal and valence ratings using the Self-Assessment Manikin scale).

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Keywords

Affect, Data Visualization, Emotion, Quantitative Study

Abstract

Emotion is an important factor to consider when designing visualizations as it can impact the amount of trust viewers place in a visualization, how well they can retrieve information and understand the underlying data, and how much they engage with or connect to a visualization. We conducted five crowdsourced experiments to quantify the effects of color, chart type, data trend, data variability and data density on emotion (measured through self-reported arousal and valence). Results from our experiments show that there are multiple design elements which influence the emotion induced by a visualization and, more surprisingly, that certain data characteristics influence the emotion of viewers even when the data has no meaning. In light of these findings, we offer guidelines on how to use color, scale, and chart type to counterbalance and emphasize the emotional impact of immutable data characteristics.