“Isms” in Visualization

Chen, Min; Edwards, Darren J.

doi:10.1007/978-3-030-34444-3_11

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…However, to people who had learned to read log scale charts, not using log scale is also misleading due to its exponential nature (Figure 3h). The discussion of breaking conventions is always fuzzy because we do not have an agreed set of conventions [CE20]. It becomes even trickier when considering its shifting nature across cultures and time.…”

Section: What Have We Learned From Misinformative Visualizations?mentioning

confidence: 99%

Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?

Gupta

Shigyo

et al. 2022

Computer Graphics Forum

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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. CCS Concepts• Human-centered computing → Information visualization;From the taxonomy and the notes taken during the coding process, we identified four directions that would enrich the discussion on misleading visualizations: (1) informal fallacies in visualizations, (2) exploiting conventions and data literacy, (3) deceptive tricks in uncommon charts, and (4) providing design guidelines by understanding the designers' dilemma.

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Section: What Have We Learned From Misinformative Visualizations?mentioning

confidence: 99%

Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?

Gupta

Shigyo

et al. 2022

Computer Graphics Forum

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“…Streeb et al collected a large number of arguments and found that many arguments were in conflict with each other [ 60 ]. Chen and Edwards presented an overview of schools of thought in the field of visualization, and showed that the “why” question was a bone of major contention [ 61 ]. The most common argument about the “why” question is because visualization offers insight or helps humans to gain insight.…”

Section: Appendix A1 An Information-theoretic Measure For Cost-benefi...mentioning

confidence: 99%

A Bounded Measure for Estimating the Benefit of Visualization (Part II): Case Studies and Empirical Evaluation

Chen

Abdul‐Rahman

Silver

et al. 2022

Entropy

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Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss due to a variety of many-to-one mappings. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence, undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed information-theoretic measure for analyzing the cost–benefit ratio of visualization processes can explain such usefulness experienced in practice and postulate that the viewers’ knowledge can reduce the potential distortion (e.g., misinterpretation) due to information loss. This suggests that viewers’ knowledge can be estimated by comparing the potential distortion without any knowledge and the actual distortion with some knowledge. However, the existing cost–benefit measure consists of an unbounded divergence term, making the numerical measurements difficult to interpret. This is the second part of a two-part paper, which aims to improve the existing cost–benefit measure. Part I of the paper provided a theoretical discourse about the problem of unboundedness, reported a conceptual analysis of nine candidate divergence measures for resolving the problem, and eliminated three from further consideration. In this Part II, we describe two groups of case studies for evaluating the remaining six candidate measures empirically. In particular, we obtained instance data for (i) supporting the evaluation of the remaining candidate measures and (ii) demonstrating their applicability in practical scenarios for estimating the cost–benefit of visualization processes as well as the impact of human knowledge in the processes. The real world data about visualization provides practical evidence for evaluating the usability and intuitiveness of the candidate measures. The combination of the conceptual analysis in Part I and the empirical evaluation in this part allows us to select the most appropriate bounded divergence measure for improving the existing cost–benefit measure.

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“…Streeb et al collected a large number of arguments and found many arguments are in conflict with each other [8]. Chen and Edwards presented an overview of schools of thought in the field of visualization, and showed that the "why" question is a bone of major contention [9].…”

Section: It Can Explain Why Visualization Is Usefulmentioning

confidence: 99%

A Short Introduction to Information-Theoretic Cost-Benefit Analysis

Chen

2021

Preprint

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This arXiv report provides a short introduction to the information-theoretic measure proposed by Chen and Golan in 2016 for analyzing machine-and human-centric processes in data intelligence workflows. This introduction was compiled based on several appendices written to accompany a few research papers on topics of data visualization and visual analytics. Although the original 2016 paper and the follow-on papers were mostly published in the field of visualization and visual analytics, the cost-benefit measure can help explain the informative trade-off in a wide range of data intelligence phenomena including machine learning, human cognition, language development, and so on. Meanwhile, there is an ongoing effort to improve its mathematical properties in order to make it more intuitive and usable in practical applications as a measurement tool.

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“Isms” in Visualization

Cited by 9 publications

References 35 publications

Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?

Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?

A Bounded Measure for Estimating the Benefit of Visualization (Part II): Case Studies and Empirical Evaluation

A Short Introduction to Information-Theoretic Cost-Benefit Analysis

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