How Deceptive are Deceptive Visualizations?

Abstract

In this paper, we present an empirical analysis of deceptive visualizations. We start with an in-depth analysis of what deception means in the context of data visualization, and categorize deceptive visualizations based on the type of deception they lead to. We identify popular distortion techniques and the type of visualizations those distortions can be applied to, and formalize why deception occurs with those distortions. We create four deceptive visualizations using the selected distortion techniques, and run a crowdsourced user study to identify the deceptiveness of those visualizations. We then present the findings of our study and show how deceptive each of these visual distortion techniques are, and for what kind of questions the misinterpretation occurs. We also analyze individual differences among participants and present the effect of some of those variables on participants' responses. This paper presents a first step in empirically studying deceptive visualizations, and will pave the way for more research in this direction.

The Deceptive Potential of Common Design Tactics Used in Data Visualizations

“…This holds true across varying amounts of data shown in the graphs, as there were only two data points in the bar and bubble graphs, seven in the pie, and eleven in the line graph. These results confirm Pandey et al [5] and O'Brien & Lauer [3], while extending those studies to show deceptiveness in the new genres of a line graph with a truncated axis and a pie graph with a 3D bevel effect.…”

“…Only recently have scientists started to empirically test the extent to which people are actually deceived by deceptive tactics used in data visualizations [3-5, 21, 25]. Pandey et al was the first to empirically show that participants were more likely to be misled in their interpretations of data visualizations that employed deceptive tactics such as message reversal (e.g., an inverted axis) and message exaggeration (e.g., truncated y-axis) [5]. Following this, O'Brien and Lauer found that people's perception of information presented in deceptive data visualizations persisted even when the deceptive graphic was paired with a paragraph of accurate text [3].…”

“…The deceptive potential of data visualizations has been discussed by practitioners and theorists [2][3][4][5], and they have made recommendations for how developers can avoid inadvertently deceiving those to whom they are communicating data [2,6,7]. Such recommendations include starting y-axes at zero, framing data broadly enough to accurately represent trends, avoiding 3-D effects that can misrepresent the size or angle of data points, and avoiding randomly spaced data points; though there is still some debate over whether these recommendations should be adhered to at all times, or whether there are instances in which they can be discarded depending on the focus of the data being communicated [8][9][10][11].…”

Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

“…[5] states that the model prefers to include zero for continuous fields and that violating the rule increases the cost of the visualization by 5. A soft constraint is appropriate: though omitting a zero baseline for ratio data can mislead [42], it is still sometimes reasonable to do. Note that a visualization may violate a soft constraint multiple times.…”

“…Visualization designers benefit from familiarity with both the data domain under consideration and principles of effective visual encoding. Although designers can learn these principles from books, research papers, and experience, they do not always follow these principles in practice [6,42]. Automated design tools [37,66] are designed to help address this problem: they use formally-encoded design guidelines to promote effective visualizations.…”