Showing People Behind Data: Does Anthropomorphizing Visualizations Elicit More Empathy for Human Rights Data?
Abstract
We investigate the impact of using anthropomorphized data graphics over standard charts on viewers' empathy for, and prosocial behavior toward suffering populations, in the context of human rights narratives. We present a series of experiments conducted on Amazon Mechanical Turk, in which we compare various forms of anthropomorphized data graphics-ranging from a single human figure that "fills up" to show proportional data, to separated groups of individual human beings-with a standard chart baseline. Each experiment uses two carefully crafted human rights data-driven stories to present the graphics. Contrary to our expectations, we consistently find that anthropomorphized data graphics and standard charts have very similar effects on empathy and prosocial behavior.
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Nudging with Narrative Visualization: Communicating to a Young Adult Audience in the Pandemic
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FaceVis: Exploring a Robot's Face for Affective Visualisation Design
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Visualization for VillainyPreprint
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Mapping the Landscape of COVID-19 Crisis VisualizationsPreprint
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RETRACTED: A Perception Study for Unit Charts in the Context of Large-Magnitude Data Representation
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RetractedFeeling numbers : the rhetoric of pathos in visualization
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Cognitive Affordances in Visualization: Related Constructs, Design Factors, and Framework
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Trouble at Sea: Data and digital technology challenges for maritime human rights concerns
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- Authors
- Boy, J., Pandey, A. V., Emerson, J., Satterthwaite, M., Nov, O., & Bertini, E.
- Affiliations
- United Nations (US)
- New York University (US)
- Venue
- Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2017-05-02
- Pages
- 5462-5474
- OpenAlex
- W2587329347
- Cited by
- 104
- References
- 51
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 16 · 2023: 12 · 2022: 12 · 2021: 16 · 2020: 12 · 2019: 9 · 2018: 9 · 2017: 3
- FWCI
- 2.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- John D. And Catherine T. MacArthur Foundation (G-107779-0)
- 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 › Arts and Humanities › Literature and Literary Theory › Media Influence and Health
- Keywords
- data visualization, empathy, prosocial behavior, human rights narratives, data storytelling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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