作者
Leo Yu‐Ho Lo, Ayush Gupta, Kento Shigyo, Aoyu Wu, Enrico Bertini, Huamin Qu
发表日期
2022/6
期刊
Computer Graphics Forum
卷号
41
期号
3
页码范围
515-525
简介
Data visualization is powerful in persuading an audience. However, when it is done poorly or maliciously, a visualization may become misleading or even deceiving. Visualizations give further strength to the dissemination of misinformation on the Internet. The visualization research community has long been aware of visualizations that misinform the audience, mostly associated with the terms “lie” and “deceptive.” Still, these discussions have focused only on a handful of cases. To better understand the landscape of misleading visualizations, we open‐coded over one thousand real‐world visualizations that have been reported as misleading. From these examples, we discovered 74 types of issues and formed a taxonomy of misleading elements in visualizations. We found four directions that the research community can follow to widen the discussion on misleading visualizations: (1) informal fallacies in …
引用总数
学术搜索中的文章
L Yu-Ho Lo, A Gupta, K Shigyo, A Wu, E Bertini, H Qu - arXiv e-prints, 2022