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Using Generative Art to Convey Past and Future Climate Transitions

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Quantifying the development of user-generated art during 2001-2010.

Mehrdad Yazdani¹, Jay Chow², Lev Manovich³

¹California Institute for Telecommunications and Information Technology's Qualcomm Institute, University of California San Diego, San Diego, California, United States of America.

|August 10, 2017

Summary

This study quantitatively analyzes visual art trends on DeviantArt from 2001-2010. It reveals gradual, systematic changes in subjects, techniques, and visual characteristics across Traditional and Digital Art categories over time.

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Area of Science:

Digital Humanities
Computational Social Science
Art History

Background:

Understanding cultural and artistic evolution is a key humanities question.
Previous research used computational methods for literature, music, and cinema, but not visual art on social networks.
DeviantArt, a large online art community, provides a unique dataset for studying visual art trends.

Purpose of the Study:

To conduct the first quantitative analysis of historical changes in visual art from a social online network.
To develop and apply computational methods for analyzing temporal art image development.
To investigate changes in subjects, techniques, size, proportions, and visual characteristics of artworks.

Main Methods:

Utilized a dataset of 270,000 artworks from DeviantArt (2001-2010).
Developed novel computational methods for analyzing temporal art image evolution.
Classified artworks into Traditional Art and Digital Art to assess the impact of digital tools.

Main Results:

Identified gradual and systematic changes in visual art over a ten-year period.
Observed shifts in subjects, techniques, and visual characteristics within both Traditional and Digital Art.
The study provides insights into how digital tools may influence artistic content and form.

Conclusions:

Computational analysis of large-scale online art datasets can reveal significant cultural and artistic trends.
The study demonstrates the utility of quantitative methods in understanding the evolution of visual art.
Findings contribute to the digital humanities by providing empirical evidence of art historical changes.