Effects of Alternative Scatterplot Designs on Belief

要旨

Viewers tend to underestimate correlation in positively correlated scatterplots. However, systematically changing the size and opacity of scatterplot points can bias estimates upwards, correcting for this underestimation. Here, we examine whether the application of these visualisation techniques goes beyond a simple perceptual effect and could actually influence beliefs about information from trusted news sources. We present a fully-reproducible study in which we demonstrate that scatterplot manipulations that are able to correct for the correlation underestimation bias can also induce stronger levels of belief change compared to conventional scatterplots presenting identical data. Consequently, we show that novel visualisation techniques can be used to drive belief change, and suggest future directions for extending this work with regards to altering attitudes and behaviours.

著者
Gabriel Strain
University of Manchester, Manchester, United Kingdom
Andrew J. Stewart
University of Manchester, Manchester, United Kingdom
Caroline Jay
University of Manchester, Manchester, United Kingdom
Charlotte Rutherford
University of Manchester, Manchester, United Kingdom
Paul A. Warren
University of Manchester, Manchester, United Kingdom
DOI

10.1145/3706598.3713809

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713809

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Data Interpretation and Storytelling

G418+G419
6 件の発表
2025-04-28 23:10:00
2025-04-29 00:40:00
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