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.
Keywords
- Deception
- Data visualization
- Data dissemination
- Statistical analysis
- Data ethics
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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. https://doi.org/10.1007/978-3-319-59186-5_8
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