Social network (sociolinguistics)

In the field of sociolinguistics, social network describes the structure of a particular speech community. Social networks are composed of a "web of ties" (Lesley Milroy) between individuals, and the structure of a network will vary depending on the types of connections it is composed of. Social network theory (as used by sociolinguists) posits that social networks, and the interactions between members within the networks, are a driving force behind language change.

The key participant in a social network is the anchor, or center individual. From this anchor, ties of varying strengths radiate outwards to other people with whom the anchor is directly linked. These people are represented by points. Participants in a network, regardless of their position, can also be referred to as actors or members.

There are multiple ways to describe the structure of a social network. Among them are density, member closeness centrality, multiplexity, and orders. These metrics measure the different ways of connecting within of a network, and when used together they provide a complete picture of the structure of a particular network.

A social network is defined as either "loose" or "tight" depending on how connected its members are with each other, as measured by factors like density and multiplexity. This measure of tightness is essential to the study of socially motivated language change because the tightness of a social network correlates with lack of innovation in the population's speech habits. Conversely, a loose network is more likely to innovate linguistically.

The density of a given social network is found by dividing the number of all existing links between the actors by the number of potential links within the same set of actors. The higher the resulting number, the more dense a network is. Dense networks are most likely to be found in small, stable communities with few external contacts and a high degree of social cohesion. Loose social networks, by contrast, are more liable to develop in larger, unstable communities that have many external contacts and exhibit a relative lack of social cohesion.

Member closeness centrality is the measurement of how close an individual actor is to all the other actors in the community. An actor with high closeness centrality is a central member, and thus has frequent interaction with other members of the network. A central member of a network tends to be under pressure to maintain the norms of that network, while a peripheral member of the network (one with a low closeness centrality score) does not face such pressure. Therefore, central members of a given network are typically not the first members to adopt a linguistic innovation because are socially motivated to speak according to pre-existing norms within the network.

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