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How Deceptive are Deceptive Visualizations?: An Empirical Analysis of Common Distortion Techniques

Published: 18 April 2015 Publication History

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.

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  1. How Deceptive are Deceptive Visualizations?: An Empirical Analysis of Common Distortion Techniques

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    cover image ACM Conferences
    CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
    April 2015
    4290 pages
    ISBN:9781450331456
    DOI:10.1145/2702123
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 18 April 2015

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    Author Tags

    1. deceptive visualization
    2. empirical analysis
    3. evaluation

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    • NYU-Poly Seed Fund Grant for Collaborative Research
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    April 18 - 23, 2015
    Seoul, Republic of Korea

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    • (2025)Magnitude Judgements are Influenced by the Relative Positions of Data Points Within Axis LimitsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336406931:2(1414-1421)Online publication date: Feb-2025
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