Shaun O'Brien
ABSTRACT
In this paper we present the results of an empirical study that analyzed how people understand the data presented to them in deceptive data visualizations when those visualizations are paired with non-deceptive text. This study was administered as an online user survey and was designed to test the extent to which deceptive data visualizations can fool users, even when they are accompanied by a paragraph of accurate text. The study consisted of a basic demographic questionnaire, chart familiarity assessment, and data visualization survey. A total of 256 participants completed the survey and were evenly distributed between a control (non-deceptive) survey and a test (deceptive) survey in which participants were asked to observe a paragraph of text and a data visualization. Participants then answered a question relevant to the observed information to measure how they perceived the information. The results of the study confirmed that deceptive techniques in data visualizations caused participants to misinterpret the information in the deceptive data visualizations even when they were accompanied by accurate explanatory text. Furthermore, certain demographics and comfort levels with chart types were more susceptible to certain types of deceptive techniques. These results highlight the importance of education and awareness in the area of data visualizations to ensure deceptive practices are not utilized on the part of developers and to avoid misinformation on the part of users.
- Kirk, A. (2012). Data Visualization: a successful design process. Birmingham: Packt Publishing.Google Scholar
- Monmonier, M. S. (1991). How to lie with maps. Chicago: University of Chicago Press.Google Scholar
- Tufte, E. R. (1983). The visual display of quantitative information. Cheshire, CT.: Graphics Press. Google ScholarDigital Library
- Pandey, A. V., Rall, K., Satterthwaite, M. L., Nov, O., & Bertini, E. (2015, April). How deceptive are deceptive visualizations?: An empirical analysis of common distortion techniques. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1469--1478). ACM. Google ScholarDigital Library
- Huff, D. (1954). How to lie with statistics. London: WW Norton & Company.Google Scholar
- Jones, G. E. (2011). How to lie with charts. LaPuerta Books and Media.Google Scholar
- Manovich, L. (2011). What is visualisation?. Visual Studies, 26(1), 36--49.Google ScholarCross Ref
- Keim, D., Mansmann, F., Schneidewind, J., & Ziegler, H. (2006). Challenges in Visual Data Analysis. Information Visualization, 2006. IV 2006. Tenth International Conference on, 9--16. Google ScholarDigital Library
- Kerren, A., Stasko, J. T., Fekete, J,. North, C., Purchase, H. C., Andrienko, N., Ward, M. (n.d.). Information visualization: Human-centered Issues and perspectives /. Berlin;: Springer. Google ScholarDigital Library
- Chen, C., Härdle, W., & Unwin, A. (2008). Handbook of Data Visualization (Springer Handbooks Comp. Statistics). Berlin, Heidelberg: Springer Berlin Heidelberg. Google ScholarDigital Library
- Pasternack, S., & Utt, S. (1989). Reader Use and Understanding of Newspaper Informational Graphics. S.l.}: Distributed by ERIC Clearinghouse.Google Scholar
- Wainer, H. (1984). How to Display Data Badly. The American Statistician, 38 (2), 137--147.Google Scholar
- Sue, V and Griffin, M. (2016). Data Visualization and Presentation with Microsoft Office. Thousand Oaks, CA: Sage.Google Scholar
- Schriver, K. (1997). Dynamics in document design: Creating text for readers. New York: Wiley Computer Publishing. Google ScholarDigital Library
- Bertin, J. (1983). Semiology of graphics. Madison, Wis.: University of Wisconsin Press. Google ScholarDigital Library
- Cleveland, W., & Mcgill, R. (1984). Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 79 (387), 531--554.Google ScholarCross Ref
- Rogowitz, B., Treinish, & Bryson. (1996). How Not to Lie with Visualization. Computers in Physics, 10 (3), 268--273. Google ScholarDigital Library
- Elting, L. S., Martin, C. G., Cantor, S. B., & Rubenstein, E. (1999). Influence of data display formats on physician investigators' decisions to stop clinical trials: Prospective trial with repeated measures. British Medical Journal, 318 (7197), 1527--1531.Google ScholarCross Ref
- Bowen, J. P., Keene, S., & Ng, K., (2013). Electronic visualization in arts and culture. London: Springer. Google ScholarDigital Library
Index Terms
- Testing the Susceptibility of Users to Deceptive Data Visualizations When Paired with Explanatory Text
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