  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.
