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

Abstract

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 feature-tuning in glyph design can influence overall effectiveness of novel multidimensional encodings.