It is clear that the online social networks have a profound influence on the social and information retrieval activities. Content distributed on social network platforms is a unique model that challenges mainstream and traditional networks of communication. Computer-mediated-communication (CMC) creates new methods of connectivity between individuals and groups. These networks have varied range between users, are centrally focused on individuals, and assign implicit online community roles. The social network facilitates the flow of information through direct and indirect ties and as a result defines how people acquire resources and information (Garton, 1997). CMC plays a central role as an inter-mediator of information. Contemporary social networking platforms have rapidly grown because of their ability to effectively link individuals and groups of networks with one another –i.e. Facebook, Twitter, Linkedin, and YouTube. The efficiency of CMC enables users to be connected to a number of different networks defined by different relationships and different communication medium. These relationships or ties, identify new networks, thus CMC creates networks that aggregate shared resources and information (Craven & Wellman, 1973, p.g59). The increase of acquired users on social networking platforms fabricates more networks and groups of networks that therefore have increasingly prominent impacts on the acquisition of information and media content.
The ability to produce and reproduce content is also a factor that dramatically alters perceptions of new media. The format, the audience, and the volume through CMC channels are redefining media’s intention and application. The vast amount of content online is pressuring the efficiency of media distribution channels, substantiated by user expectations for the web to affiliate content with context. Social networks play a significant role in facilitating context. They do this by evaluating ties between people, groups, and pages. Social networks are described as entities in a network called ‘nodes’ and the connection between them called ‘ties’, representing a matrices that allows filtering content specific to an individual (Downes, 2005, p.416). Haythornthwaite (2001) argues that there is a tendency for social networks to enhance the distribution of media but stronger ties within these networks remains centred on pre-existing local networks or affiliations; niche networks are often weaker ties in the network. Conversely, Downes (2005) presents the studies of “six-degrees” network measurement (Milgram, 1967) revealing that there are only six steps in a network between every person in the United States (p.417). More so then ever, this network phenomenon is accurate but also applicable on a global scale using CMC networks ties which are both easy to create and effectively measurable. Although this conclusion is justified, the effects of the “long tail”– many small elements of information that make up an equal or larger proportion of the Internet –require networks and ties to define relationships to niche content specific to the user. The accuracy of distributing niche content to specific individuals is highly valued during information retrieval process because of the volume of content that is available. Nevertheless, this retrieval process requires complex relational ties that leverage social network data.
Strong ties are of great relevance to social networks and the distribution of media on social networking platforms. Marsden and Campbell (1984) distinguish the combination of factors that distinguish ties in a network: frequency of contact, duration of association, intimacy of the tie, provision of reciprocal services, and kinship (p484). Currently, this measurement of ties attempts to link the “social web” with content. Google, the biggest search engine on the web, bases its model on content-centric consumption. The shortfall of this model is that it neglects to meet the complex information retrieval process necessary to retrieve content in the long tail of the web. The social web on the other hand, adds user’s personal information as a variable in the search. The social web has supporters includes Microsoft’s Bing search engine and Facebook. Their partnership in October 2010 will enable Bing searchers to leverage Facebook profile ties to influence content retrieval; known as the user-centric model of content consumption (Oreskovic, 2010).
Browsing the web is not longer passive, user have participative roles on a websites; “individuals are now more apt to have their behaviours, likes, and dislikes automatically integrated in proprietarily databases” (Elmer, 2002, p.86). The exploitation of this user data will significantly influence content that is distributed to the user and although the relational model is only in its early stages of implementation, the shear volume of activity that is recorded by social networking sites, generate opportunities to customise searchable content and media. The current social networking platforms are maturing rapidly as user acquisition is very high. Nevertheless, methods of transferring content are still primal as they are submissive in the online environment. Presently, social networks only marginally influence content distribution – i.e. Friend-of-a-Friend (FoaF), advertising, and recommendations.