Illegible Semantics: Exploring the Design Space of Metal Logos

The logos of metal bands can be by turns gaudy, uncouth, or nearly illegible. Yet, these logos work: they communicate sophisticated notions of genre and emotional affect. In this paper we use the design considerations of metal logos to explore the space of “illegible semantics”: the ways that text can communicate information at the cost of readability, which is not always the most important objective. In this work, drawing on formative visualization theory, professional design expertise, and empirical assessments of a corpus of metal band logos, we describe a design space of metal logos and present a tool through which logo characteristics can be explored through visualization. We investigate ways in which logo designers imbue their text with meaning and consider opportunities and implications for visualization more widely.

[1]  Cullen D. Jackson,et al.  Using Close Reading as a Method for Evaluating Visualizations , 2020, 2020 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV).

[2]  D. Weiskopf,et al.  Hagrid — Gridify Scatterplots with Hilbert and Gosper Curves , 2021, VINCI.

[3]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Aram Ter-Sarkisov.  Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos , 2020, ICPRAM.

[5]  Karrie Karahalios,et al.  Frames and Slants in Titles of Visualizations on Controversial Topics , 2018, CHI.

[6]  Will Eisner,et al.  Comics and Sequential Art: Principles and Practices from theLegendary Cartoonist , 2008 .

[7]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[8]  Ronald A. Rensink.  On the Prospects for a Science of Visualization , 2014, Handbook of Human Centric Visualization.

[9]  S. Franconeri,et al.  Why Shouldn’t All Charts Be Scatter Plots? Beyond Precision-Driven Visualizations , 2020, 2020 IEEE Visualization Conference (VIS).

[10]  Oded Nov,et al.  Showing People Behind Data: Does Anthropomorphizing Visualizations Elicit More Empathy for Human Rights Data? , 2017, CHI.

[11]  Anne R. Richards.  Argument and Authority in the Visual Representations of Science , 2003 .

[12]  Stefan Jänicke,et al.  A Survey on Visualizations for Musical Data , 2020, Comput. Graph. Forum.

[13]  Helen Kennedy,et al.  The work that visualisation conventions do , 2016 .

[14]  Steven Franconeri,et al.  The Curse of Knowledge in Visual Data Communication , 2019, IEEE Transactions on Visualization and Computer Graphics.

[15]  Charles Perin,et al.  Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude , 2020, Comput. Graph. Forum.

[16]  Robert Kosara,et al.  The Shaping of Information by Visual Metaphors , 2008, IEEE Transactions on Visualization and Computer Graphics.

[17]  C. Kostelnick.  The Visual Rhetoric of Data Displays: The Conundrum of Clarity , 2007, IEEE Transactions on Professional Communication.

[18]  Michael Gleicher,et al.  Bad for Data, Good for the Brain : Knowledge-First Axioms For Visualization Design , 2014 .

[19]  J. Bertin.  Classification typographique: Voulez-vous jouer avec mon A , 1980 .

[20]  T. V. Leeuwen.  Towards a semiotics of typography , 2006 .