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Understanding and Measuring the Effects of Graphical Dimensions on Viewers' Perceived Chart Credibility (2305.20079v1)

Published 31 May 2023 in cs.HC

Abstract: Journalists and visualization designers include visualizations in their articles and storytelling tools to deliver their message effectively. But design decisions they make to represent information, such as the graphical dimensions they choose and the viewer's familiarity with the content can impact the viewer's perceived credibility of charts. Especially in a context where little is known about sources of online information. But there is little experimental evidence that designers can refer to make decisions. Hence, this work aims to study and measure the effects of graphical dimensions and people's familiarity with the content on viewers' perceived chart credibility. I plan to conduct a crowd-sourced study with three graphical dimensions conditions, which are traditional charts, text annotation, and infographics. Then I will test these conditions on two user groups, which are domain experts and non-experts. With these results, this work aims to provide chart guidelines for visual designers with experimental evidence.

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