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2017 | OriginalPaper | Chapter

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

Authors : Athir Mahmud, Mél Hogan, Andrea Zeffiro, Libby Hemphill

Published in: Big Data Factories

Publisher: Springer International Publishing

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Abstract

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.

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Title: Teaching Students How (Not) to Lie, Manipulate, and Mislead with Information Visualization
Authors: Athir Mahmud
Mél Hogan
Andrea Zeffiro
Libby Hemphill
Publisher: Springer International Publishing
Book: Big Data Factories
Print ISBN: 978-3-319-59185-8

Electronic ISBN: 978-3-319-59186-5

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-59186-5_8

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