Skip to main content

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

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

This is a preview of subscription content, access via your institution.

Fig. 8.1
Fig. 8.2
Fig. 8.3
Fig. 8.4
Fig. 8.5
Fig. 8.6


  1. 1.

  2. 2.


Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Athir Mahmud .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Mahmud, A., Hogan, M., Zeffiro, A., Hemphill, L. (2017). Teaching Students How (Not) to Lie, Manipulate, and Mislead with Information Visualization. In: Matei, S., Jullien, N., Goggins, S. (eds) Big Data Factories. Computational Social Sciences. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59185-8

  • Online ISBN: 978-3-319-59186-5

  • eBook Packages: Computer ScienceComputer Science (R0)