Annotating Line Charts for Addressing Deception

Fan, Arlen; Ma, Yuxin; Mancenido, Michelle V.; Maciejewski, Ross

doi:10.1145/3491102.3502138

Cited by 22 publications

(11 citation statements)

References 29 publications

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“…More specifcally, several aspects of visualization misinformation have been studied previously. For instance, some have looked at line charts and addressed deception in line charts by adding annotations [22]. Others have studied misalignment between a visualization and its title and how this impacts trust and recall of information [27].…”

Section: Related Work 21 Visualization Misinformationmentioning

confidence: 99%

CALVI: Critical Thinking Assessment for Literacy in Visualizations

Ge¹,

Yuan²,

Kay³

2023

Preprint

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Visualization misinformation is a prevalent problem, and combating it requires understanding people’s ability to read, interpret, and reason about erroneous or potentially misleading visualizations, which lacks a reliable measurement: existing visualization literacy tests focus on well-formed visualizations. We systematically develop an assessment for this ability by: (1) developing a precise definition of misleaders (decisions made in the construction of visualizations that can lead to conclusions not supported by the data), (2) constructing initial test items using a design space of misleaders and chart types, (3) trying out the provisional test on 497 participants, and (4) analyzing the test tryout results and refining the items using Item Response Theory, qualitative analysis, a wrong-due-to-misleader score, and the content validity index. Our final bank of 45 items shows high reliability, and we provide item bank usage recommendations for future tests and different use cases. Related materials are available at: https://osf.io/pv67z/.

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Section: Related Work 21 Visualization Misinformationmentioning

confidence: 99%

CALVI: Critical Thinking Assessment for Literacy in Visualizations

Ge¹,

Yuan²,

Kay³

2023

Preprint

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“…However, some line charts are deceptive with exaggeration, understatement, and message reversal. To address this problem, Fan et al [32] introduced a tool for detecting and annotating line graphs in the wild that reads line graph images and outputs text and visual annotations to assess the truthfulness of line graphs and help readers understand graph data in good faith.…”

Section: Annotated Chartmentioning

confidence: 99%

How Does Automation Shape the Process of Narrative Visualization: A Survey on Tools

Chen¹,

Cao²,

Wang³

et al. 2022

Preprint

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In recent years, narrative visualization has gained a lot of attention. Researchers have proposed different design spaces for various narrative visualization types and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, more and more tools have been designed and developed. In this paper, we surveyed 122 papers and tools to study how automation can progressively engage in the visualization design and narrative process. By investigating the narrative strengths and the drawing efforts of various visualizations, we created a two-dimensional coordinate to map different visualization types. Our resulting taxonomy is organized by the seven types of narrative visualization on the +x-axis of the coordinate and the four automation levels (i.e., design space, authoring tool, AI-supported tool, and AI-generator tool) we identified from the collected work. The taxonomy aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

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“…Yet, most existing research aimed at seeking solutions for misleading visualizations is still focused on identifying violations of design guidelines and blatant errors using automatic annotation and linting [14,21,41], although every work also acknowledges the importance of studying visualization errors that stem from biased reading. Several frameworks for thinking about data visualizations through the lens of cognitive biases have been proposed [5,11].…”

Section: Misleading Visualizationsmentioning

confidence: 99%

“…The visualization community has been primarily defining misleading visualizations as charts that interfere with the viewer's ability to accurately read off and compare values. The terms "deceptive", "misleading", "lying" are typically used to describe visualizations with visual tricks, such as truncated or inverted axes, or the violation of visualization guidelines and best practices, such as the use of unjustified 3D or problematic color maps [14,49,65]. This type of visual deception is rooted in the gap between the true value of data points used as input for the chart and the different values perceived by the viewer.…”

Section: Introductionmentioning

confidence: 99%

Misleading Beyond Visual Tricks: How People Actually Lie with Charts

Lisnic¹,

Polychronis²,

Lex³

et al. 2022

Preprint

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Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event---Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters' arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation online.

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Annotating Line Charts for Addressing Deception

Cited by 22 publications

References 29 publications

CALVI: Critical Thinking Assessment for Literacy in Visualizations

CALVI: Critical Thinking Assessment for Literacy in Visualizations

How Does Automation Shape the Process of Narrative Visualization: A Survey on Tools

Misleading Beyond Visual Tricks: How People Actually Lie with Charts

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