Explorative visualization and analysis of a social network for arts: the case of deviantART

deviantArt is one of the leading social online network sites with a focus on user generated artworks. The website has a rich data archive of around 150 million images uploaded by its 15 million members, making it the largest art platform of today. This paper describes an open source toolkit that provides a humanities scholar with necessary computational tools to analyze and visualize deviantArt and similar arts collections. To this effect, we combine tools of different research fields such as network analysis, computer vision, machine learning and data visualization. The toolkit provides functionality to extract data about members and their artworks directly from the deviantArt website, using network analysis to select key members for further investigation. The chosen members’ images are downloaded, and annotated automatically with different image features, along which they can be visualized. The visualization options offered in the implemented toolkit link images to their originals, and can be used to explore and analyze the dataset in an interactive way. The toolkit also features an SVM-based classifier to automatically select features to discriminate artists, artworks and styles, which is hidden from the user behind a simple ‘suggest features’ option.