Introduction to the Special Issue on Social Linking and Hypermedia

One of the most exciting recent developments in Web Science is the rise of social annotation, by which users can easily markup other authors’ resources via collaborative mechanisms, such as tagging, filtering, voting, editing, classification, and rating. These phenomena are showing up in all kinds of Web 2.0 applications, such as social networking platforms, wikis, blogs, and collaborative tools for tagging, voting, or commenting. Social annotations lead to the emergence of many types of links between pages, users, concepts, articles, media, and so on. The motivation and interaction patterns in the context of social media are of high research interest because they allow us to investigate digital traces of human activities. The objective of this special issue is to gain deeper insights into the world of social media through new ways of analyzing and modeling of the emergent links. Both papers deal with the analysis of networks from a mathematical, graph theoretical perspective, but the analyzed networks are very different. Kaltenbrunner, Gonzalez, de Querol, and Volkovich (Comparative analysis of articulated and behavioural social networks in a social news sharing website) present a detailed analysis of a friendship (explicit) and a conversation (implicit) network. Besides the usual analysis for each of these networks a comparison between the networks is given. The comparison reveals different behavior patterns in reply interactions in online conversation between friends and non-friends, with reply interactions more likely to occur between non-friends. Neubauer and Obermayer (Tripartite community structure in social bookmarking data), on the other hand, focus on hypergraphs that capture the underlying structure of social bookmarking systems. In their contribution they present two new methods for community detection. To show the benefits of these community detection methods, synthetic and real world data from the popular bookmarking system, Delicious, are used. The paper demonstrates how tripartite community detection can help understand the structure of social bookmarking data. New Review of Hypermedia and Multimedia, Vol. 17, No. 3, December 2011, 241 242