The Persuasive Power of Data Visualization
Abstract
Data visualization has been used extensively to inform users. However, little research has been done to examine the effects of data visualization in influencing users or in making a message more persuasive. In this study, we present experimental research to fill this gap and present an evidence-based analysis of persuasive visualization. We built on persuasion research from psychology and user interfaces literature in order to explore the persuasive effects of visualization. In this experimental study we define the circumstances under which data visualization can make a message more persuasive, propose hypotheses, and perform quantitative and qualitative analyses on studies conducted to test these hypotheses. We compare visual treatments with data presented through barcharts and linecharts on the one hand, treatments with data presented through tables on the other, and then evaluate their persuasiveness. The findings represent a first step in exploring the effectiveness of persuasive visualization.
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People can be more easily persuaded by tabular designs of data when they hold strong attitudes against the depicted topic, but more easily persuaded by bar graphs when they have no strong attitudes.
“People can be more easily persuaded by tabular designs of data when they hold strong attitudes against the depicted topic, but more easily persuaded by bar graphs when they have no strong attitudes [34].”
From Illusion of Causality in Visualized Data (2020) · cites this paper as [34]
Persuasiveness of visualized data depends on both context and visualization designs.
“Prior work suggests that persuasiveness of visualized data depends on both context (does the topic align with the viewers' prior beliefs?) and visualization designs (tabular design or bar graphs) [25, 26, 34].”
From Illusion of Causality in Visualized Data (2020) · cites this paper as [34]
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- Authors
- Pandey, A. V., Manivannan, A., Nov, O., Satterthwaite, M., & Bertini, E.
- Affiliations
- New York University (US)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2014-12-31
- Volume
- 20
- Issue
- 12
- Pages
- 2211-2220
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2013728941
- Cited by
- 217
- References
- 36
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 21 · 2023: 20 · 2022: 23 · 2021: 40 · 2020: 15 · 2019: 18 · 2018: 15 · 2017: 18 · 2016: 7 · 2015: 6 · 2014: 1
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- Digital Commons @ The University of Maryland, Baltimore Carey Law (The University of Maryland, Baltimore) (submittedVersion)
- Funders
- NSF Award (IIS-1149745)
- NYU-Poly Seed Fund Grant
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- data visualization, persuasion, visual communication, bar charts, line graphs, tables, user influence
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