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Emoji and Chernoff - A Fine Balancing Act or are we Biased?

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Ricardo Colasanti, Rita Borgo, Mark W. Jones

Original languageEnglish
Title of host publicationIEEE Pacific Visualization Symposium (PacificVis 2019)
Number of pages10
Accepted/In press14 Jan 2019


King's Authors


We seek to answer the question on whether different geometrical
attributes within a glyph can bias interpretation of data. We focus on
a specific visual encoding, the Emoji, and evaluate its effectiveness
at encoding multidimensional features. Given the anthropomorphic
nature of the encoding we seek to quantify the amount of bias
the encoding itself introduces, and use this to balance the Emoji
glyph to remove that bias. We perform our analysis by comparing
Emoji with Chernoff faces, of which they can be seen as direct
descendant. Results shed light on how this new approach of featuretuning
in glyph design can influence overall effectiveness of novel
multidimensional encodings.

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