A Deep Study into the History of Web Design

Abstract

Since its ambitious beginnings to create a hyperlinked information system, the web has evolved over 25 years to become our primary means of expression and communication. No longer limited to text, the evolving visual features of websites are important signals of larger societal shifts in humanity's technologies, aesthetics, cultures, and industries. Just as paintings can be analyzed to study an era's social norms and culture, techniques for systematically analyzing large-scale archives of the web could help unpack global changes in the visual appearance of websites and of modern society itself. In this paper, we propose automated techniques for characterizing the visual "style" of websites and use this analysis to discover and visualize shifts over time and across website domains. In particular, we use deep Convolutional Neural Networks to classify websites into 26 subject areas (e.g., technology, news media websites) and 4 design eras. The features produced by this process then allow us to quantitatively characterize the appearance of any given website. We demonstrate how to track changes in these features over time and introduce a technique using Hidden Markov Models (HMMs) to discover sudden, significant changes in these appearances. Finally, we visualize the features learned by our network to help reveal the distinctive visual elements that were discovered by the network.