How People Are Influenced by Deceptive Tactics in Everyday Charts and Graphs

O'Brien, Shaun

doi:10.1109/tpc.2020.3032053

Cited by 24 publications

(16 citation statements)

References 26 publications

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“…Analyzing Cherry Picking Filter out data that is not conducive to the author's intended narrative [10] The Simpson's paradox high-level aggregation of data leads to wrong conclusion [21] Visualizing Break Conventions Create unusual charts that mislead people to analyze them with conventions [13,31,45] Concealing Uncertainty Conceal the uncertainty in the chart to cover up the low quality of the data [50] Scripting Text-visualization misalignment The message of the text differs from that of the visualization it refers to [26,27] Text Wording…”

Section: Causes Of Misinformation Descriptionmentioning

confidence: 99%

“…Prior research has investigated misinformation existed in standalone visualization, e.g., bar chart [31,45], thematic maps [12], etc. However, few studies are concerned with how misinformation can be introduced in narrative visualization.…”

Section: Introductionmentioning

confidence: 99%

“…This idea is inspired by the fact that different framing of the narrative visualization can result in different interpretations towards the same data story [22]. Previous studies show that relying on one-sided information to understand data stories may partially result in the unawareness of deceptive visualizations and text-visualization misalignment [27,31]. Motivated by this observation, in our study, we considered candidate design methods that can enhance the integration of text and visualization, thus balancing readers' interaction with textual and visual information.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Effect of Enhanced Text-Visualization Integration on Combating Misinformation in Data Story

Zheng¹,

Ma²

2022

Preprint

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Misinformation has disruptive effects on our lives. Many researchers have looked into means to identify and combat misinformation in text or data visualization. However, there is still a lack of understanding of how misinformation can be introduced when text and visualization are combined to tell data stories, not to mention how to improve the lay public's awareness of possible misperceptions about facts in narrative visualization. In this paper, we first analyze where misinformation could possibly be injected into the productionconsumption process of data stories through a literature survey. Then, as a first step towards combating misinformation in data stories, we explore possible defensive design methods to enhance the reader's awareness of information misalignment when data facts are scripted and visualized. More specifically, we conduct a between-subjects crowdsourcing study to investigate the impact of two design methods enhancing text-visualization integration, i.e., explanatory annotation and interactive linking, on users' awareness of misinformation in data stories. The study results show that although most participants still can not find misinformation, the two design methods can significantly lower the perceived credibility of the text or visualizations. Our work informs the possibility of fighting an infodemic through defensive design methods.

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Section: Causes Of Misinformation Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the Effect of Enhanced Text-Visualization Integration on Combating Misinformation in Data Story

Zheng¹,

Ma²

2022

Preprint

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“…McNutt et al [54] describe a wide family of errors that can be forced upon users from across the visualization pipeline, to cause what they term visualization mirages. Lauer and O'Brien [48] describe and demonstrate the deceptive power of a variety of misleading tactics.…”

Section: Image Control (Non-physical Indirect)mentioning

confidence: 99%

Visualization for Villainy

McNutt,

Huang,

Koenig

2021

Preprint

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Visualization has long been seen as a dependable and trustworthy tool for carrying out analysis and communication tasks-a view reinforced by the growing interest in applying it to socially positive ends. However, despite the benign light in which visualization is usually perceived, it carries the potential to do harm to people, places, concepts, and things. In this paper, we capitalize on this negative potential to serve an underrepresented (but technologically engaged) group: villains. To achieve these ends, we introduce a design space for this type of graphical violence, which allows us to unify prior work on deceptive visualization with novel datadriven dastardly deeds, such as emotional spear phishing and unsafe data physicalization. By charting this vile charting landscape, we open new doors to collaboration with terrifying domain experts, and hopefully, make the world just a bit worse.

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“…The more beautiful versions of the graphs were plotted with high image resolution (300 dpi), legible font size, Sans font type, and saturated color. The less beautiful version of the graphs were plotted with low image resolution (18 dpi for maps; 26 dpi for other graph types), smaller font size, Comic Sans MS font type, and desaturated color (grayscale for line plots and scatter plots; 50% saturation for other graph types).The manipulation of graph misleadingness was based on previous research on best practices for data visualization(4)(5)(6)(7)(8)(9). Specifically, the more misleading version of the bar plot was truncated (starting from above zero) to exaggerate the relative group differences.…”

mentioning

confidence: 99%

Visualization aesthetics bias trust in science, news, and social media

Lin¹,

Thornton²

2021

Preprint

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Scientists, policymakers, and the public increasingly rely on data visualizations – such as COVID tracking charts, weather forecast maps, and political polling graphs – to inform important decisions. The aesthetic decisions of graph-makers may produce graphs of varying visual appeal, independent of data quality. Here we tested whether the beauty of a graph influences how much people trust it. Across three studies, we sampled graphs from social media, news reports, and scientific publications, and consistently found that graph beauty predicted trust. In a fourth study, we manipulated both the graph beauty and misleadingness. We found that beauty, but not actual misleadingness, causally affected trust. These findings reveal a source of bias in the interpretation of quantitative data and indicate the importance of promoting data literacy in education.

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How People Are Influenced by Deceptive Tactics in Everyday Charts and Graphs

Cited by 24 publications

References 26 publications

Evaluating the Effect of Enhanced Text-Visualization Integration on Combating Misinformation in Data Story

Evaluating the Effect of Enhanced Text-Visualization Integration on Combating Misinformation in Data Story

Visualization for Villainy

Visualization aesthetics bias trust in science, news, and social media

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