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

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  • Mehrdad Yazdani
  • Jay Chow
  • Lev Manovich

Abstract

One of the main questions in the humanities is how cultures and artistic expressions change over time. While a number of researchers have used quantitative computational methods to study historical changes in literature, music, and cinema, our paper offers the first quantitative analysis of historical changes in visual art created by users of a social online network. We propose a number of computational methods for the analysis of temporal development of art images. We then apply these methods to a sample of 270,000 artworks created between 2001 and 2010 by users of the largest social network for art—DeviantArt (www.deviantart.com). We investigate changes in subjects, techniques, sizes, proportions and also selected visual characteristics of images. Because these artworks are classified by their creators into two general categories—Traditional Art and Digital Art—we are also able to investigate if the use of digital tools has had a significant effect on the content and form of artworks. Our analysis reveals a number of gradual and systematic changes over a ten-year period in artworks belonging to both categories.

Suggested Citation

  • Mehrdad Yazdani & Jay Chow & Lev Manovich, 2017. "Quantifying the development of user-generated art during 2001–2010," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-24, August.
    • Handle: RePEc:plo:pone00:0175350
    DOI: 10.1371/journal.pone.0175350
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    Cited by:

    1. Teresa Duarte Martinho, 2018. "Researching Culture through Big Data: Computational Engineering and the Human and Social Sciences," Social Sciences, MDPI, vol. 7(12), pages 1-17, December.

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