What Makes COVID-19 Charts Understandable? A Visualization Complexity Framework for Evaluating Data Visualizations in Pandemic Media Coverage

Abstract

As citizens increasingly rely on data to navigate complex scientific issues, effective visualization has become essential. This study proposes a multidimensional framework to assess visualization complexity. We collected 6,641 COVID-19 visualizations from 29 media outlets over 18 months and recruited U.S. participants (N = 905) to evaluate a subset (N = 640). Results indicate that traditional measures of visual complexity had limited effects on audience perceptions, whereas data encodings, particularly advanced chart types and higher variable count, significantly shaped audience evaluations. These findings offer practical insights for enhancing the clarity and effectiveness of data visualizations in science communication.

Keywords

data visualization COVID-19 visual complexity processing fluency aesthetic appeal credibility

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