Teaching Students How (Not) to Lie, Manipulate, and Mislead with Information Visualization

  • Athir MahmudEmail author
  • Mél Hogan
  • Andrea Zeffiro
  • Libby Hemphill
Part of the Computational Social Sciences book series (CSS)


The authors explore the intellectual and pedagogical implications of big data visualizations. Representing data visually implies simplifying and essentializing information. However, the selective nature of information visualization can lend itself to lies, manipulations, and misleading information. To avoid these pitfalls, data analysts should focus and embrace specific principles and practices that aim to represent complete, contextualized, comparable, and scalable information in a way that reveals rather than isolates the viewer and the problem at hand from the problem space it reflects.


Deception Data visualization Data dissemination Statistical analysis Data ethics 


  1. Berger, J. (1972). Ways of seeing. London: Penguin.Google Scholar
  2. Bivens, R. (2015). The gender binary will not be deprogrammed: Ten years of coding gender on Facebook. New Media & Society, 1461444815621527. 1461444815621527.
  3. Bowker, G. C. (2006). Memory practices in the sciences. Cambridge, Mass: MIT Press.Google Scholar
  4. D’Ignazio, C. (2015). What would feminist data visualization look like? Retrieved June 16, 2016, from
  5. D’Ignazio, C. (2016). A primer on non-binary gender and big data. Retrieved June 16, 2016, from
  6. Easterling, K. (2014). Extrastatecraft: The Power of Infrastructure Space. Verso.Google Scholar
  7. Galloway, A. (2011). Are some things unrepresentable? Theory, Culture & Society, 28(7–8), 85–102. Scholar
  8. Gillespie, T. (2016). Facebook trending: It’s made of people!! (but we should have already known that). Retrieved from
  9. Gitelman, L. (2013). Raw data is an oxymoron. Retrieved June 16, 2016, from
  10. Harding, S. (1996). Feminism, science and the anti-enlightenment critiques. In A. Garry & M. Pearsall (Eds.), Women, knowledge, and reality: Explorations in feminist philosophy (2nd ed., pp. 298–320). Boston: Unwin Hyman.Google Scholar
  11. Irani, L. (2015). Difference and dependence among digital workers: The case of Amazon mechanical Turk. South Atlantic Quarterly, 114(1), 225–234. Scholar
  12. Keim, D., Qu, H., & Ma, K. L. (2013). Big-data visualization. IEEE Computer Graphics and Applications, 33(4), 20–21. Scholar
  13. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Thousand OAks: SAGE Publications.Google Scholar
  14. MacEachren, A. M. (1995). How maps work: Representation, visualization, and design. Guilford Press.Google Scholar
  15. Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the digital humanities (pp. 460–475). Minneapolis: University of Minnesota Press.CrossRefGoogle Scholar
  16. Munster, A. (2009). Data undermining: The work of networked art in an age of imperceptibility. Retrieved from Scholar
  17. Roberts, S. (2016). Digital refuse: Canadian garbage, commercial content moderation and the global circulation of social media’s waste. Wi: Journal of Mobile Media. Retrieved from
  18. Seaver, N. (2013). Knowing algorithms. Presented at the Media in Transition 8, Cambridge, MA. Retrieved from
  19. Stalbaum, B. (2004). An interpretive framework for contemporary database practice in the arts. Presented at the College Art Association 94th annual conference, Boston, MA. Retrieved from
  20. Starfield, B., Shi, L., & Macinko, J. (2005). Contribution of primary care to health systems and health. Milbank Quarterly, 83(3), 457–502.
  21. Tufte, E. R. (1997). Visual explanations: Images and quantities, evidence and narrative. Cheshire: Graphics Press.zbMATHGoogle Scholar
  22. Yau, N. (2011). Visualize this: The flowing data guide to design, visualization, and statistics. Chichester: Wiley.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Athir Mahmud
    • 1
    Email author
  • Mél Hogan
    • 2
  • Andrea Zeffiro
    • 3
  • Libby Hemphill
    • 1
  1. 1.Illinois Institute of TechnologyChicagoUSA
  2. 2.University of CalgaryCalgaryCanada
  3. 3.McMaster UniversityHamiltonCanada

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