A Deep Study into the History of Web Design

Bardia Doosti, David J. Crandall, Norman Makoto Su
2017 Proceedings of the 2017 ACM on Web Science Conference - WebSci '17  
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
more » ... b 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, signi cant 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. CCS CONCEPTS • Information systems → Surfacing; • Human-centered computing → Web-based interaction; • Computing methodologies → Interest point and salient region detections; Supervised learning by classi cation; Neural networks; • Mathematics of computing → Kalman lters and hidden Markov models;
doi:10.1145/3091478.3091503 dblp:conf/websci/DoostiCS17 fatcat:zewnhcnp2vbetpxhngztzv6dee