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.
Structure
Participants 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.
Relationships 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.Wardhaugh, Ronald (2006). An Introduction to Sociolinguistics. New York: Wiley-Blackwell. 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.
Density 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.Bergs, A. (2005). Social Networks and Historical Sociolinguistics: Studies in Morphosyntactic Variation in the Paston Letters. Berlin: Walter de Gruyter. 22-37. The higher the resulting number, the denser 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.Trudgill, Peter (2010). Investigations in Sociohistorical Linguistics. Cambridge: University Press. 61-92.
Member closeness centrality 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.Milroy, L. (1987). Language and Social Networks. New York: Blackwell. Therefore, central members of a given network are typically not the first members to adopt a linguistic innovation because they are socially motivated to speak according to pre-existing norms within the network.Milroy, Lesley (2002). "Social Networks". In: The Handbook of Language Variation and Change. Oxford. Blackwell. 549-572.
Multiplexity Multiplexity is the number of separate social connections between any two actors. It has been defined as the "interaction of exchanges within and across relationships".Bliemel, M.J., McCarthy, I.P., & Maine, E. (2014). "An Integrated Approach to Studying Multiplexity in Entrepreneurial Networks". Entrepreneurship Research Journal, 4(4), 367-402. A single tie between individuals, such as a shared workplace, is a uniplex relationship. A tie between individuals is multiplex when those individuals interact in multiple social contexts. For instance, A is B's boss, and they have no relationship outside of work, so their relationship is uniplex. However, C is both B's coworker and neighbor, so the relationship between B and C is multiplex, since they interact with each other in a variety of social roles.
Orders Orders are a way of defining the place of a speaker within a social network. Actors are classified into three different zones depending on the strength of their connection to a certain actor.Milroy, L. (1980). Language and Social Networks. Oxford: Blackwell. The closer an individual's connection to the central member is, the more powerful an individual will be within their network. Social network theories of language change look for correlation between a speaker's order and their use of prestigious or non-prestigious linguistic variants.
First order zone A first order zone is composed of all individuals that are directly linked to any given individual. The first order zone can also be referred to as the "interpersonal environment"Rossi, Peter H. 1966. "Research strategies in measuring peer group influence," Pp. 190-214 in College Peer Grotips, edited by Theodore h4. Newcomb and Everett K. Wilson. Chicago: Aldine. or "neighborhood". A first order member of a network is an actor who has a large number of direct connections to the center of the network. Second order zone A second order zone is a grouping of any individuals who are connected to at least one actor within the first order zone. However, actors in the second order zone are not directly connected to the central member of the network. A second order member has a loose or indirect connection to the network, and may only be connected to a certain network member. Third order zone A third order zone is made up of newly observed individuals not directly connected to the first order zone.Milroy, Leslie and Matthew Gordon (2008). "The Concept of Social Network". "Sociolinguistics: Method and Interpretation". Oxford: John Wiley & Sons. 116-133. Third order members may be connected to actors in the second order zone, but not the first. They are peripheral members of the network, and are often the actors with the lowest member closeness centrality, since they may not have frequent contact with other members of the network.
Language change
Sociolinguistic research
Fieldwork Social networks are used in sociolinguistics to explain linguistic variation in terms of community norms, rather than broad categories like gender or race. Instead of focusing on the social characteristics of speakers, social network analysis concentrates on the relationships between speakers, then considers linguistic change in the light of those relationships. In an effort to depart from variationist sociolinguistics,E.g., in the opening chapter of The Handbook of Language Variation and Change (ed. Chambers et al., Blackwell 2002), J.K. Chambers writes that "variationist sociolinguistics had its effective beginnings only in 1963, the year in which William Labov presented the first sociolinguistic research report"; the dedication page of the Handbook says that Labov's "ideas imbue every page". the concept of the social network has been used to examine the links between the strength of network ties and the use of a linguistic variant. This allows researchers to create an accurate picture of a community's language use without resorting to stereotypical classification.
The concept of social networks is applicable at both the macro and micro levels. Social networks are at work in communities as large as nation-states or as small as an online dating service. They can also be applied to intimate social groups such as a friendship, family unit, or neighborhood. Because even the smallest of networks contains an enormous number of potential connections between actors, sociolinguists usually only study small networks so that the fieldwork is manageable. In fact, even when studying small networks, sociolinguists rely on the metrics outlined in the previous section, rather than mapping the network out, one connection at a time. One way of mapping the general structure of a network is to assign a strength scale to each speaker. For example, in Lesley Milroy's study of social networks in Belfast, Northern Ireland, the researchers measured five social variables, which together generated a strength scale for each member of the network: (1) Membership of a high-density, territorially based cluster. (2) Having substantial ties of kinship in the neighborhood (more than one household, in addition to his own nuclear family). (3) Working at the same place as at least two others from the same area. (4) The same place of work as at least two others of the same sex from the area. (5) Voluntary association with work mates in leisure hours. The allocation of a network strength score allows the network patterns of individuals to be measured and possible links with linguistic patterns to be tested.Marshall, Jonathan (2004). Language Change and Sociolinguistics: Rethinking Social Networks (Palgrave Studies in Language Variation). Basingstoke: Palgrave Macmillan.
Computational modeling In recent years, computer simulation and modeling have been used to study social networks from a broader perspective.Bergs, A. (2006). Analyzing online communication from a social network point of view: questions, problems, perspectives. Language@Internet, 3, article 3. (urn:nbn:de:0009-7-3712)Swarup, S., Apolloni, A. & Fagyal, Z. (2011). A model of norm emergence and innovation in language change.. In L. Sonenberg, P. Stone, K. Tumer & P. Yolum (eds.), AAMAS (p./pp. 693-700), : IFAAMAS. . Because previous social network studies were focused on individual connections, the size of the networks were limited so that the researcher could work personally with subjects. With the rise of advanced computer modeling techniques, sociolinguists have been able to study the linguistic behavior of large networks of individuals over long periods of time without the inconvenience of individually working with thousands of subjects.
Advances in computer simulation and modeling technology have been used to study social networks on a larger scale, both with more participants and over a greater span of time. Previous social network studies had to examine individual connections in great detail, and so had to limit the size of the networks involved. Linguists working in the field were also unable to accurately pinpoint the causes of linguistic change because it tends to occur slowly over a long period of time, on a scale beyond the scope of a single research project. With the rise of computer modeling, sociolinguists have been able to study the linguistic behavior of large networks without the huge expenditure of time required to individually work with thousands of subjects long-term. The pioneering study in this field was Fagyal et al. in 2011.
Social network theory Because social networks investigate the forces that impact individual behavior, rather than simply attributing linguistic difference to social class, a theory of language change based on social networks is able to explain linguistic behavior more deeply than variationist sociolinguistics. The two major findings of social network theory are that dense (highly interconnected) networks are resistant to change, and that most linguistic change is initiated by weak links—people who are not centrally connected to the network in question. Though most sociolinguists working on social networks agree on these findings, there has been extended debate about which actors in the network are the primary drivers of linguistic change. The results of this debate are two theories, the strong-tie theory, and the weak-tie theory.
Strong tie theory