DEVIANTART IN SPOTLIGHT : A NETWORK OF ARTISTS

deviantArt (dA) is the largest online community of user-generated artworks. So far, a scholarly study of dA has been missing. The main goal of this paper is to describe several tools for the network analysis of this community and to propose future research directions for understanding this collaborative and autonomous art venue. Launched in 2000, deviantArt is one of the largest online communities showcasing user-made artworks. With its 19 million members, 100 million images and 45 million monthly visitors (all as of May 2012), surpassing any real or virtual museum, dA offers a genuine virtual space for disseminating art. It generates a platform free of institutional and governmental politics, democratizing the way arts are generated, shared and enjoyed, mainly through the underlying social network that allows distributed valorization of arts. Unlike Flickr [1-2], which focuses on photographs only, dA hosts a variety of genres, offering (and even enforcing) a delicate category structure to its users. Thus, all artwork is organized according to a comprehensive category structure, from photographs to various digital and traditional art forms. Each member of the site has their own webpage featuring a gallery, a journal, a favorites section, as well as a basic information box highlighting statistics such as number of visitors, number of comments, number of downloads etc. These statistics build up the main evaluation system of the dA community; a member with a large number of visitors/ comments is seen as successful. The information box also contains demographic data (gender/ geographic location/ age), and details about membership. This rich background information allows us to study the dynamics of dA via network analysis. In this paper, we highlight two aspects: 1) the social structure, where we characterize dA in terms of artists watching each other, 2) the category structure, where each category is represented with a node. The number of artists publishing in any two categories is converted to weighted edges between the categories, showing the practical affinity between categories. A Visualization Tool In order to supplement network analysis, we have developed a visualization tool that allows us to depict galleries of artists or artworks of a category [3]. There exist several tools to visualize large image collections, but these tools are geared towards similaritybased image search or content-based image retrieval. Our tool is designed for the analysis of similarities between artists and categories, and for discovering artworks with unexpected visual qualities. It projects a large number of images onto selected feature spaces (about a hundred features implemented), but it is also able to suggest the most discriminative feature space, given two sets of images. Our experiments with this tool show that dealing with the whole network for any given task is too unwieldy. For this purpose, we have extracted a representative core network. The core of deviantART The dA network consists of 13 million members, but some of the members are passive users. In order to get to the vibrant core of the dA network, we have used a number of assumptions that weed out most of the members. This helped us reach a manageable and relevant set of users. The first heuristic we used is the subscription status; the paying members of the site are more serious users and have access to more services. These can be automatically determined through scraping. Our first data reduction followed these members, and we thus obtained a network with 103.663 vertices and about 4,5 million arcs, the latter representing a user being ‘watched’ by another user (average degree is 43,25). This is referred to as the member network in the rest of the paper. We have used the member network in the analysis of the category structure, subject of the next section. Watchers get notifications about the activities of the members they are following. Thus, if a member has a high number of watchers, he/she is able to reach out to a bigger audience. This property guides us in capturing the core of dA. From the member network, we have recursively removed nodes (and all connecting arcs) that had only a few watchers. Each iteration of this k-core procedure peels off one shell from the peripheries of the network, leaving us finally with a densely connected graph [4]. Fig. 1 shows nodes of the member network according to the number of outgoing (watched) and incoming (watchers) arcs, in a log-log plot. The nodes in the core are shown in a different color, depending on the removal. The three different core networks are artists that are power-watchers, popular artists and lastly a mix of both. The statistics are shown in Table 1. LG denotes the characteristic path length (average shortest path length between vertices). CG denotes the directional clustering coefficient, which indicates social grouping. Lrandom and Crandom denote these statistics for a typical Fig. 1. Member Network and the Core Networks superposed (watchers core, watched core, and the mixed core). (© Almila Akdag Salah Copyright Holder.) random graph of the same size. Table 1. Core Network Statistics Statistic Watchers Watched Mixed core Nr. of nodes 1701 1471 1099 Nr. of arcs 139.285 127.837 166.244 Avg. degree 81,88 86,90 151,27