What Eye Tracking Can Show Us About How People Are Influenced by Deceptive Tactics in Line Graphs

Claire Lauer Arizona State University ; Christopher A. Sanchez Oregon State University

Abstract

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> Graphs, especially those that are generated automatically, are often subject to mistakes in their processing, framing, and construction, sending unintended messages that neither the viewer nor the author may realize. This article analyzes the eye-tracking data of 57 participants to extend the results of a previous study that investigated how people are deceived by common mistakes and deceptive tactics in data visualizations and titles. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review:</b> Previous research has suggested that viewers are susceptible to deception by misleading titles or graph presentations, and that such information can influence how they interpret graphs. Previous eye-tracking research has only measured viewing patterns of nondeceptive graphs. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions:</b> 1. How much attention do participants give to various areas of a graph when not given any instruction on what to look for, nor what they might be asked about? 2. Are there differences in how participants view and interpret deceptive versus control graphs about noncontroversial topics? 3. Are there differences in how participants view and interpret graphs about noncontroversial topics paired with control or exaggerated titles? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</b> This study analyzed view time, fixations, revisits, and time to first fixation for the graph area, title, y-axis, and x-axis of four line graphs. Qualitative responses were also coded and analyzed. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</b> Among other significant findings, this study found that participants spent significantly less time looking at both line graph axes for graphs with a rhetorically exaggerated title than those with a control title. Participants also fixated on and revisited deceptive graphs more so than control graphs, and fixated and revisited the title and x-axis of control graphs significantly more than deceptive graphs. Qualitative results contribute further patterns. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Discussion:</b> Findings suggest that graphs with exaggerated titles make viewers less attentive to the axes, but deceptive graphs cause viewers to examine the lines of the graphs themselves in greater detail. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</b> Subtle changes in the makeup of graphics can significantly change how viewers examine such visualizations. It is critical to better understand how these changes influence viewing and how they might be leveraged to ultimately impact understanding.

Journal
IEEE Transactions on Professional Communication
Published
2023-09-01
DOI
10.1109/tpc.2023.3290948
CompPile
Open Access
Closed
Export

Citation Context

Cited by in this index (3)

  1. Journal of Technical Writing and Communication
  2. Communication Design Quarterly
  3. IEEE Transactions on Professional Communication

References (35) · 1 in this index

  1. How to Lie with Statistics
  2. 10.1109/MCG.2021.3132004
  3. The Visual Display of Quantitative Information
  4. Visualization for villainy
  5. How to Lie With Maps
Show all 35 →
  1. How to lie with charts
  2. 10.1145/3411764.3445443
  3. 10.1109/TVCG.2013.234
  4. 10.2307/2683253
  5. Fixation durations-why are they so highly variable?
    Zur Leistungsf&#x00E4;higkeit Der Rational-Choice-Theorie
  6. 10.1145/2968219.2968326
  7. 10.1145/3290605.3300576
  8. Why are Americans shooting strangers and neighbors? &#x2018;it all goes back to fear&#x2019;
    The Washington Post
  9. Crime data explorer
  10. 10.1109/TVCG.2014.2346320
  11. 10.1145/2702123.2702608
  12. 10.1145/3491102.3502138
  13. 10.1145/3233756.3233961
  14. How people read graphs
    Proc Conf Res Pract Inf Technol
  15. 10.1007/978-3-319-47024-5_14
  16. 10.1037/1076-898X.4.2.75
  17. 10.1007/s10648-011-9174-7
  18. Fooled by beautiful data: Visualization aesthetics bias trust in science, news, and social
  19. 10.4324/9781351550932-5
  20. 10.1207/s1532799xssr1003_3
  21. 10.1145/2470654.2470696
  22. 10.1187/cbe.18-06-0102
  23. 10.1109/TVCG.2015.2467732
  24. 10.1177/0963662514549688
  25. 10.1145/3173574.3174012
  26. 10.1109/VAST.2017.8585665
  27. 10.2307/2288400
  28. IEEE Transactions on Professional Communication
  29. 10.1109/TVCG.2011.255
  30. 10.1109/TVCG.2016.2598594