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Social network (sociolinguistics) - Wikipedia, the free encyclopedia. In the field of sociolinguistics, social network describes the structure of a particular speech community. Social networks are composed of a . 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. The black lines represent connections between individuals. Participants. 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. 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 . Conversely, a loose network is more likely to innovate linguistically. Density. 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. 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. It has been defined as the . 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. Actors are classified into three different zones. Visualization of snowball sampling technique showing two sampling zones. The first- order zone contains 7 individuals (black nodes). An Introduction To Sociolinguistics Pdf FreeThe second- order zone contains individuals that have direct contact to individuals in the first- order zone. The circles indicate the boundaries of the zones. Social network theories of language change look for correlation between a speaker's order and their use of prestigious or non- prestigious linguistic variants. 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 . 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. A third order zone is made up of newly observed individuals not directly connected to the first order zone. 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. In an effort to depart from variationist sociolinguistics. 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. 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. 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. UNIVERSITY OF PUNE Revised Course Structure of English S. Compulsory English (w. To develop competence among the students. An Introduction To Sociolinguistics Pdf Janet HolmesIn other words, CDA aims to investigate critically social inequality as it is expressed, constituted, and. The english language Grounded in linguistic research and argumentation, The English Language: From Sound to Sense offers readers who have little or no analytic. 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. 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. Though most sociolinguistics 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. These agents also regulate language usage inside the network, and therefore ensure the dominance of their preferred variant form throughout the network, because group members are more likely to adopt high- status variants. Strong tie networks are believed to be resistant to linguistic innovation, because prestige forms also tend to be conservative forms. According to strong- tie theory, this is how new variants are spread. William Labov's study of Philadelphia speech communities provides an example of the strong- tie theory. Therefore it is the case that language change will have the propensity to be faster in larger communities rather than in smaller communities. This imitation of desirable qualities indicates that strongly connected agents lead change by spreading norms through network members. Actors with high levels of prestige in the linguistic led the use of these forms, and enforced them as norms within the community. Members of this network then used the forms normalized within the network outside of the network, and continuous usage led to wide adoption of these speech norms. He interviewed several elementary school children, and taught them some invented words he created for the purpose of this study. After teaching the students these words, and telling them to teach the other students these words, he came back a week later to observe the results. A few children, those who were popular, friendly, cheerful, active in class activities, were the main people who spread those words. As the centers of their respective networks, these children functioned as strong- tie leaders of linguistic change. Support for the weak- tie theory. Though these second- order actors, or . This study served as the basis of the . Milroy took part in the life of each community as an acquaintance, or 'friend of a friend', investigating the correlation between the integration of individuals in the community and the way those individuals speak. Each individual studied was given a network strength score based on the person's knowledge of other people in the community, the workplace and at leisure activities to give a score of 1 to 5, with 5 being the highest network 'strength score'. Out of the five variables, one measured density, while the other four measured multiplexity. Each person's use of phonological variables, (ai), (a), (l), (th), (. The independent variables for this study were age, sex and location. These linguistic variables made up the dependent variable of the study, and were analyzed in relation to the network structure and background of each individual speaker. Deviation from the regional standard was determined by density and multiplicity of the social networks into which speakers are integrated. The researchers found that a high network strength score was correlated with the use of vernacular forms, and therefore that the use of vernacular variants was strongly influenced by the level of integration into a network. The conclusion of the study was that close- knit networks are important for dialect maintenance. Belfast: subsequent study. The researchers found that actors with the weakest tie to this community identity were most likely to use the variable . These young people often interacted with members of other social networks, and thus spread the . These results provide support for the weak tie theory of language change, because it was the actors on the peripheries of social networks who were responsible for spreading linguistic change. Bridging the two theories. They then randomly assigned a linguistic variant to each node. |
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