Analysing Social Networks Via the Internet

THe purpose of this article is to introduce the reader to the history, concepts, measures and methods of social network analysis as applied to online information spaces. This is done through description as well as a sustained example using the online social news site Digg.com. Social network analysis is a rapidly expanding interdisciplinary paradigm , much of which is taking place with online data. As such, some concepts will only be addressed superficially, while others (such as positions, p* models and multilevel analysis) will be excluded entirely. The goal is to facilitate enough network literacy to begin a research project rather than provide a complete end-to-end solution. Social network analysis has emerged in the past half-century as a compelling complement to the standard toolkit of social science researchers. At its foundation is a belief that explanations for social organization are not to be found in innate drives or abstract forces. Instead we can look to the structure of relationships that constrain and enable interaction (Wellman, 1988) alongside the behaviors of agents that reproduce and alter these structures (Emirbayer & Mische, 1998). While this paradigm has been applied to fields as diverse as sexual contacts among adolescents (Bearman, Moody, & Stovel, 2004) and intravenous drug users (Koester, Glanz, & Baron, 2005), social network analysis is particularly well suited to understanding online interaction. There are two key facts about online interaction that make it particularly amenable to social network analysis the nature of online interaction and the nature of digital information. Online interaction is almost always social network-oriented. At its simplest, social networks refer to a series of nodes (such as people, organizations or web pages) and the specific links between two of these nodes. Hypertext (such as the World Wide Web) is an unstructured series of pages and links between pages. Communication online can be represented as a network of senders and recipients. Finally, relationships on social software sites constitute an obvious series of nodes (profiles) and links (friends). As Barry Wellman muses, “when computer networks link people as well as machines they become social networks” (1996, p. 214). While digital information does not have to be networkoriented, this certainly facilitates the capture of network data. Granted, communication patterns and relationships were studied as networks long before the internet. However, collecting in-person data is time consuming and difficult; people are sometimes unclear of who is in their personal network (or how strong the tie is), and it is important to gather high response rates. These problems can be minimized online because information is digital and encoded merely through the act of sending a message or adding a friend to one’s page. Also, there is virtually no marginal cost in making a perfect replica of the messages for analysis.