论文标题
通过可视化误导：我们从错误的信息可视化中学到了什么？
Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?
论文作者
论文摘要
数据可视化在说服听众方面具有强大的功能。但是，当它做得不好或恶意地完成时，可视化可能会误导甚至欺骗。可视化为互联网上的错误信息传播提供了进一步的力量。可视化研究界长期以来已经意识到可视化误解了观众的信息，这主要与“谎言”和“欺骗性”术语相关。尽管如此，这些讨论仅集中在少数案件上。为了更好地理解误导性可视化的景观，我们对据报道是误导的一千多个现实世界进行了开放编码。从这些示例中，我们发现了74种类型的问题，并形成了可视化元素误导性元素的分类法。我们发现研究界可以遵循的四个方向扩大了有关误导性可视化的讨论：（1）可视化的非正式谬论，（2）利用惯例和数据素养，（3）毫无常见的图表中的欺骗技巧，以及（4）（4）理解设计师的困境。这项工作为这些研究方向奠定了基础，尤其是在理解，检测和防止它们方面。
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 visualizations, (2) exploiting conventions and data literacy, (3) deceptive tricks in uncommon charts, and (4) understanding the designers' dilemma. This work lays the groundwork for these research directions, especially in understanding, detecting, and preventing them.
