Misleading Beyond Visual Tricks: How People Actually Lie with Charts

Lisnic, Maxim; Polychronis, Cole; Lex, Alexander; Kogan, Marina

doi:10.1145/3544548.3580910

Cited by 8 publications

(10 citation statements)

References 61 publications

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“…Yet, one does not even need to use fake information, only to have an automated tool capable of choosing convenient half‐truths to support a position, querying the needed data and then creating beautiful and convincing visualizations that can be easily spread through unregulated social media channels. Further, most visual deception carried out on social media does not even require graphically tricking the user, rather data mirages like cherry‐picking are more than sufficient [ LPLK23 ].…”

Section: Discussionmentioning

confidence: 99%

Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

Schetinger

Bartolomeo

El‐Assady³

et al. 2023

Computer Graphics Forum

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Section: Discussionmentioning

confidence: 99%

Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

Schetinger

Bartolomeo

El‐Assady³

et al. 2023

Computer Graphics Forum

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“…Wrong? To answer this question, we can turn to our previous work that outlined reasoning errors in social media users' interpretations of data visualizations [32]: cherry-picking favorable subsets of data, assigning causality to salient features of charts, or not accounting for common statistical fallacies. In this work, we focus on cherry-picking-one of the most often used tactics-as an illustrative example.…”

Section: What Can Gomentioning

confidence: 99%

“…Interventions that target biases and fallacies in narrative visualizations or at the audience level include using textual warnings against assuming that correlation equals causation [28], attaching multiple views to combat visualization mirages [56], adding interactive linking between text and data [55], as well as design alternatives for highlighting the truncation of the vertical axis [12]. Although the visualization community has raised concerns about the role of cherry-picked charts in the spread of misinformation across numerous studies [20,21,32,33], to the best of our knowledge, this is the first work specifically attempting to design interventions against cherry-picking.…”

Section: Interventions Against Fallacies In Data Visualizationsmentioning

confidence: 99%

“…Such data visualizations, when shared on social media, are often misrepresented in a way that supports wrong and, in the worst case, harmful arguments. We have previously shown that cherry-picking of items and time frames is an issue that contributed to misinformation arguments in over 40% of COVID-skeptic charts shared on Twitter, most of which were screenshots of data explorers [32].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we describe the design space of visualization guardrails and implement a prototype data explorer with four distinct guardrails. We limit our designs to line charts showing time-series data because we previously found this to be the dominant form of charts and data used for cherry-picking in a social media context [32]. In order to investigate both the production and the reaction problems, we conducted two crowd-sourced experiments using our prototype of guardrails that target item cherry-picking.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visualization Guardrails: Designing Interventions Against Cherry-Picking in Interactive Data Explorers

Lisnic,

Cutler,

Kogan

et al. 2024

Preprint

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The growing popularity of interactive time series exploration platforms has made visualizing data of public interest more accessible to general audiences. At the same time, the democratized access to professional-looking explorers with preloaded data enables the creation of convincing visualizations with carefully cherry-picked items. Prior research shows that people use data explorers to create and share charts that support their potentially biased or misleading views on public health or economic policy and that such charts have, for example, contributed to the spread of COVID-19 misinformation. Interventions against misinformation have focused on post hoc approaches such as fact-checking or removing misleading content, which are known to be challenging to execute. In this work, we explore whether we can use visualization design to impede cherry-picking—one of the most common methods employed by deceptive charts created on data exploration platforms. We describe a design space of guardrails—interventions against cherry-picking in time series explorers. Using our design space, we create a prototype data explorer with four types of guardrails and conduct two crowd-sourced experiments. In the first experiment, we challenge participants to create cherry-picked charts. We then use these charts in a second experiment to evaluate the guardrails’ impact on the perception of cherry-picking. We find evidence that guardrails—particularly superimposing relevant primary data—are successful at encouraging skepticism in a subset of experimental conditions but come with limitations. Based on our findings, we propose recommendations for developing effective guardrails for visualizations.

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Navigating Visual Information: Understanding Audience Perception and Evaluation in Different Data Visualizations

Ch’ng,

Looi

2024

ITM Web Conf.

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In the age of Big Data where data literacy is vital across diverse domains, the prevalence of misleading visualization raises significant concerns. Examining the extent of such visualizations is crucial since viewers often lack the ability to choose the form of presentation. This study aims to investigate the impact of intentionally misleading data visualizations on cognitive biases by exploring factors that potentially influence perceptions and evaluations. A factorial experimental design with three factors involving methods of data visualization, audience academic background and the sequence of data presentation is employed. A total of 60 undergraduate students with two different major programmes from a local higher educational institution participated in this experiment. These students were tasked with responding to predesigned questions based on two different sets of infographics addressing the same issues. The findings indicate that both data presentation and analytical background significantly influence audience evaluation. Additionally, the order of data presentation reveals that audience evaluation is influenced by their initial negative impression. These results underscore the critical role of data literacy in enhancing the understanding of visual information, particularly in the context of public issues.

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Misleading Beyond Visual Tricks: How People Actually Lie with Charts

Cited by 8 publications

References 61 publications

Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

Visualization Guardrails: Designing Interventions Against Cherry-Picking in Interactive Data Explorers

Navigating Visual Information: Understanding Audience Perception and Evaluation in Different Data Visualizations

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