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Social network (sociolinguistics)

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In the field of sociolinguistics, social network is a term used to describe a particular speech community in terms of the relations between individual members of that community. Social networks are viewed as the smaller micro-level communities, comprised of a web of ties anchored to individuals and relating persons to others with whom they directly and indirectly interact. This is in opposition to social classes, which are instead thought of as macro-level structures. Group members within a network interact with varying frequency, and these interactions systematically lead to language variation and subsequent language change.

Overview

Language is often understood as the cognitive ability of a human to learn and use systems of complex communication, either spoken or written. Language is inherently social, because it cannot exist without a user. Therefore, language networks must also be considered as social networks.

Social networks are organized into speech communities of various sizes and with varying degrees of density of internal connections. A network may be one that is "loose" or "tight" depending on how members interact with each other.[1] The extent of looseness or tightness of a social network affects the speech patterns adopted by a speaker. In a social network, there are accepted language norms,[2] and adherence to those norms can be more strictly enforced by persons in the group. Each person can be a member of many networks, and have many connections to a network and can result in different orders of a social network.

The concept of social network is applicable at both the macro and micro levels. This means that social networks are able to be understood as being as big as a community of actors occupying a geographic region such as a city, state, or country, or as abstract as a community of actors occupying the Internet, online dating services, chatrooms, or Facebook. They can also be as small or as interpersonal as a friendship, family unit, neighborhood, or civic organization.

Social network theory is an application of the concept of the social network to sociolinguistics. Researchers of social networks take a fine-grained approach to sociolinguistic analysis, focusing on individuals and their networks of contacts, rather than a broad social category such as social class or race, as the locus of social connection. According to Lesley Milroy, one of the main proponents of social network theory, if the social network is "an aggregate of relationships contracted with others," then the study of that social network "examines the differing structures and properties of these relationships."[3]

History

In an effort to steer sociolinguistic research away from language change as it relates to the broad sweeping notion of social class, the concept of the social network has been used to examine the links between the strength of network ties within a social network and some form of resulting linguistic data.[4] Two predominant theories of network ties relating to language change have emerged: the strong tie theory and the weak tie theory.

The strong tie theory, or agentive theory, has long been thought of in classical sociolinguistic theory as a driver of change, even prior to social network theory.[5][6][7][8] In the context of social network theory, agents are the people who are most connected to others in the network, and whose speech style is often imitated by people within the network. These agents also regulate other people’s language usage inside the group, and therefore ensure the spread of their preferred variant form throughout the network, as group members are more likely to adopt the style of those members in the group who have status. Strong tie networks are believed to be resistant to linguistic innovation and themselves serve as mechanisms that enforce the norms associated with a particular social network.[9][10]

In an effort to depart from variationist sociolinguistics[11], a discipline founded by William Labov which explores the relation between groups of people and their language use by effectively describing an individual’s language use in terms of social attributes of the individual, sociolinguists have more recently begun to focus their studies on “weak links”: networks that are not closely connected by shared socioeconomic status, or individuals who are not closely tied to any group, such as people who move frequently or live in isolated areas.[12] The weak tie theory, first proposed by Milroy and Milroy in their 1983 study of a village in Belfast, language change is seen as being propagated by the people who are second order members of the network.[13] “Weak ties provide people with access to information and resources beyond those available in their own social circle” (Granovetter, 1983).[14] Loosely connected individuals are viewed as less likely to conform to group language practices than integral members and thus bring those members of the group into contact with variations that they might otherwise never have encountered.[15][16]

In recent years, computer simulation and modeling have been used to study social networks from a broader perspective.[17][18][19] Because previous social network studies were focused on individual connections, they tended to limit the size of the network in order for the researcher to work personally with subjects. With the rise of computer modeling, sociolinguists have been able to study the linguistic behavior of large networks of individuals over long periods of time without the inconveniences associated with individually working with thousands of subjects.

Social Network Theory

Network theory tries to understand and provide an explanation for the relationships between the components of a given system. Within the field of linguistics, network theory can be better described as “the study of relations which exist in an on-going system. When applied to social systems, network analysis is a research strategy which is primarily concerned with the relations amongst individuals, or actors, in social groups.”[20] Lesley Milroy defines a social network as "a boundless web of ties which reaches out through a whole society, linking people to one another, however remotely" (Milroy, 2002).[3] Social networks are viewed as the fundamental dynamic in language change.

Strong Ties

Eckert’s studies in indexicality indicate that agents lead language change, by spreading norms through network members. In Eckert’s study of speech norms in Detroit high schools, she notes that suburban youth adopted the speech traits of urban youth (diphthongized and lowered [i]). They did so to adopt the traits they found desirable in urban youth. The suburban youth adopted these norms to project an image of toughness, wanting to act like urban youth, who were agents of change within their friendship networks.
Labov also notes in his study of philadelphia communities, that two distinct speech communities and social networks were developing their own speech norms. These communities were divided along racial lines, african american, and caucasian. The leaders of these communities, the ones with the most prestige, enforced the language norms of the communities. These norms were developed by these leaders, and are used by people with strong ties to the community leaders. These changes then filter through the network, and create long term change.

Weak ties

Social network theory postulates that linguistic variables are spread by means of weak, uni-dimensional social links between non-central individuals. Therefore it is the case that language change will have the propensity to be faster in larger communities rather than in smaller communities.[21]

Social Network: Its Structure

All speakers have a place in the social network of their social environment. One principle element in a social network is the anchor, or center individual. From this anchor person, ties of varying strengths radiate outwards to other people with whom the anchor is directly linked. These people are represented by points. These people collectively make up the zone about that anchor. A zone can be described as a bounded region, geographic or metaphoric in nature.[22]

A ‘’first order zone’’ is comprised of any individuals that are directly linked to any given individual (also referred to as the “interpersonal environment” when the first order zone is about individual persons[23], and the “neighborhood” in graph theory). A "second order zone" is a grouping of any individuals that are directly connected to the first order zone but are not themselves part of it. A "third order zone" is made up of newly observed individuals not directly connected to those in the first order zone.[24] A first order member of the network is someone who generally has a great number of direct connections to the center of the network, while a second order member has a loose or indirect connection to the network, and may only be connected to a certain network member.

Social Network Analysis

Social network analysis positions itself atop the notion that a social network can be used as an analytical device for quantitative purposes that can explain linguistic variation by a means more sophisticated than social stratification.[22] Social network analysis focuses on the structure of relationships that emerge in social networks. Instead of analyzing the characteristics of people, social network analysis concentrates on the relationships between linguistic variables and people and/or networks of people.

Central to the social network analysis are the concepts of density, member closeness centrality, and multiplexity.

The density of a given social network is the number of actual links, or ties, between actors within a set of actors, divided by the number of potential links within that set.[25] 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, in contrast, are more liable to develop in larger, unstable communities that have many external contacts and exhibit a relative lack of social cohesion.[26]

Member closeness centrality is defined as a measurement of how close an individual actor is to all the other actors in the community, or the set of actors. An actor with a high degree of closeness centrality can quickly interact with other members of the social network. A central member of a network is also under pressure to maintain the norms of that network, while a peripheral member of the network does not face such pressure.[27] Therefore, central members of a given network are never the innovators. Conversely, they are usually the last ones to adopt the innovation because they do not want to jeopardize their integral role in the network by introducing outside influences (linguistic innovations included).[3]

Multiplexity is understood as representing the number of social functions or social roles that any participants of a community fulfill in their relationship. A tie between individuals is multiplex when those individuals interact in multiple capacities. For instance, when Person A knows Person B, and the extent of "knowing" is only that of a superior or boss-type relation, then the relationship is uniplex. But, if Person B is also affiliated with Person A in a different respect, say Person B is Person A's neighbor, friend, or acquaintance, then their relationship is now multiplex, since they know each other and interact with each other in a variety of social functions or roles.[25]

In order to apply any sort of quantitative analysis to the data, Milroy proposes that a network strength scale must be allocated to each speaker. The allocation of a network index score allows the network patterns of individuals to be measured and possible links with linguistic patterns to be tested.[28]

Computational Modeling and Simulation

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.[17][18][19] 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 Leaders and Loners Study

One key study that employed computer simulations was Fagyal, Swarup, Escobar, Gasser, and Lakkarajud’s work on the roles of group insiders (leaders) and outsiders (loners) in language change.[19] In this study, the researchers simulated a social network of 900 participants, called nodes, which were connected into a network using a matrix algorithm. They then randomly assigned a linguistic variant to each node. On each cycle of the algorithm, every node interacted with another node, and the variant assigned to each node changed randomly depending on which variant the other node had. This cycle was repeated 40,000 times, and at the end of each cycle, the variant connected to each node was recorded.
The results of the Fagyal et al. study indicated that “in a large, socially heterogenous population,” one linguistic variant eventually became the community norm, though other variants were not entirely eliminated. However, when the researchers manipulated the network to remove either loners or leaders, the results changed: without loners, one variant rapidly caused the loss of all other variants; and without leaders, no single variant became the norm for a majority of speakers.

These findings allowed the researchers to address the major debate in social network theory: whether it is leaders (or centers) or loners who are responsible for language change. In their findings, the presence of both leaders and loners was essential, though the two types of agents played different roles in the process of change. As the researchers write, “centers’ most essential role is to serve as conduits for innovations to propagate to others in the network.” Rather than introducing entirely new forms, leaders accelerate the adoption of forms that already exist within the network. Conversely, the researchers describe the loners’ role this way: “when loners are a part of a population structure that allows their influence to reach centrally-connected hubs, they can have a decisive impact on the linguistic system over time.” Previously, researchers had posited that loners preserved old forms that had been neglected by the larger community. Fagyal et al. complicate this claim by suggesting that the role of loners in a network is to safeguard old features, then reintroduce them to the community. Thus, both leaders and loners are responsible for language change.

Linguistic Studies

The Harlem Study

In Labov's 1966 study of African American Vernacular English in South Harlem,[29] he discovered that it was most often people who had second order linguistic membership in African American social networks who caused changes to be propagated to mainstream communities. These people were “lames”, positioned on the end of the social network, and were people who weren’t held in high regard by the leaders of the speech network. They did, however, have connections to other networks, and were thus able to introduce linguistic variables used by members of the social network they are of which second order members.

Labov’s Harlem study served as the basis of the ‘’Weak Tie Theory’’ proposed by Milroy and Milroy's 1980 Belfast study.

The Belfast Studies

Belfast: The Original Study

Milroy and Milroy study examined vernacular English as it was spoken in inner-city Belfast in the 1970s, in three working class communities in Belfast: those in the Ballymacarrell area, the Hammer area, and the Clonard area. 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.

A five-point network strength scale used in the Belfast study (Milroy 1980: 54):[22]

(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.
  • According to Milroy, condition one is an indicator of density, while conditions two to five are indicators of multiplexity.

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.

Each person's use of eight phonological variables, (ai), (a), (l), (th), (ʌ), (ɛ), (?), and (?), that were clearly indexical of the Belfast urban speech community, were then measured. The independent variables for this study were age, sex and area. 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.

What the Milroy's found was that a high network strength score was correlated with the use of vernacular forms.

Belfast: Subsequent Study

Milroy’s 1987 study explored the usage of a rounded or unrounded realization of [u] in Belfast. An unrounded [u] is generally seen as a trait of the working class. In Ballymarket, one of the villages they surveyed, this unrounded [u] was most often used by young males and females, who don’t necessarily identify as working class members. They hold no strong tie to the working class networks, but use the variables to project an image of working-class toughness. These young people, who often interact with people of other social networks, and thus spread the [u] realization through their social networks, creating large scale change, and the adoption of unrounded [u] in Belfast. This is seen as strong evidence of the weak tie theory.

The Internet Chat rooms Study

In Berg’s 2006 study of digital social networks as sociolinguistic social networks, he notes the values of social networks as linguistic corpuses and linguistic networks.[30] Although they do not necessarily have explicit first order or second order memberships within the network, there is greater usage of certain variables within certain sub-communities. In this paper specifically, the linguistic variables seen in the #India IRC community, are mostly used by young people within a youth sub-community. These variables aren’t seen across the networks a whole, just in sub-communities. These innovations are coming from certain sub-communities, and remaining in peripheral sub-communities, different from the pattern of change extending out of sub-communities into large scale communities in traditional, non computer mediated social networks in linguistics.

The Facebook Study

In Perez-Sabater’s 2012 profile of Facebook users[31], she discusses native English speakers usage of Facebook on university Facebook pages. She categorizes these posts as a model of computer-mediated communication, a new communication style that combines features of writing and speech, and compares the usage of Facebook by native and non-native english speakers. Facebook posts, she notes, generally have a degree of formality, no matter whether native or nonnative English speakers, but native English speakers often have a higher degree of informality. She compares the informality and abbreviations used by native English speakers on these Facebook pages, which are mostly formal which often include abbreviations, a mix of capitalizations, and the usage of non-separated salutations, to those of non-native english speakers. Native English speakers often use peoples names in their posts to indicate if they are replying to a specific individual, while non-native speakers do not. In addition, non native speakers cited in the study use separated letter-style greetings and salutations, indicating linguistic insecurity (a lack of confidence in their English skills). Perez-Sabater concludes that computer-mediated communication does not always tend toward informality, and that social networks serve as a platform similar to non-virtual social networks, while also noting that further information must be found.

See also

References

  1. ^ Wardhaugh, Ronald (2006). An Introduction to Sociolinguistics. New York: Wiley-Blackwell.
  2. ^ Attention: This template ({{cite jstor}}) is deprecated. To cite the publication identified by jstor:2776392, please use {{cite journal}} with |jstor=2776392 instead.
  3. ^ a b c Milroy, Lesley (2002). "Social Networks". In: The Handbook of Language Variation and Change. Oxford. Blackwell. 549-572.
  4. ^ Milroy (1987) notes that variationist and social network practices are not necessarily at odds with one another; instead, the networks in which an individual is embedded and the larger social groups to which he belongs are “phenomena at different levels of abstraction” (133).
  5. ^ Kilduff, Martin & Wenpin Tsai. 2003. Social Networks and Organizations. London. SAGE Publications.
  6. ^ Mitchell, J. C 1969. The concept and use of social networks. See Ref. 40, I-50
  7. ^ Jacobson, D. 1972. Social Circles, Scale and Social Organization. Presented to Burg Wartenstein Symp. 25. Scale and Social Organization, Wenner-Gren Found. Anthropol. Res.
  8. ^ Barnes, J. A. 1 969. Graph theory and social networks: A technical comment on connectedness and connectivity. Sociology 3(2):215-32
  9. ^ Labov, W. 1969. ‘Contraction, Deletion, and Inherent Variability of the English Copula’. Language 45: 715-762
  10. ^ Milroy, Lesley. 1980. Language and social networks. London; Baltimore: Basil Blackwell; University Park Press. xii, 218 pages.
  11. ^ 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".
  12. ^ Attention: This template ({{cite jstor}}) is deprecated. To cite the publication identified by jstor:2776392, please use {{cite journal}} with |jstor=2776392 instead.
  13. ^ Milroy, J. and Milroy, L., et al. (1983) Sociolinguistic variation and linguistic change in Belfast. Report to SSRC (HR5777).
  14. ^ Granovetter, Mark S. 1983. The strength of weak ties: a network theory revisited. Sociological Theory 1:201–233.
  15. ^ Milroy, J. and Milroy. L., et al. (1983) Sociolinguistic variation and linguistic change in Belfast.
  16. ^ Eckert, Penelope. 2005. Variation, convention, and social meaning. Plenary talk at the Annual Meeting of the Linguistic Society of America, Oakland, CA. Retrieved 15 August 2008 from http://www.stanford.edu/~eckert/EckertLSA2005.pdf.
  17. ^ a b Fagyal, Zsuzsanna; Swarup, Samarth; Escobar, Anna Maria; Gasser, Les; and Lakkaraju, Kiran (2010) "Centers, Peripheries, and Popularity: The Emergence of Norms in Simulated Networks of Linguistic Influence," University of Pennsylvania Working Papers in Linguistics: Vol. 15: Iss. 2, Article 10.
  18. ^ a b 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)
  19. ^ a b c 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. ISBN 978-0-9826571-5-7.
  20. ^ Wei, Li (1996): “Network Analysis”. In: Goebl, Hans et al. (eds.): Kontaktlinguistik/contact linguistics/linguistique de contact: ein internationales Handbuch zeitgenössischer Forschung/an international handbook of contemporary research/manuel international des recherches contemporaines. Berlin/New York: Walter de Gruyter, 805.
  21. ^ Trudgill, Peter (2001). Extract from Peter Trudgill (2002). Sociolinguistic Variation and Change. Edinburgh: Edinburgh University Press.
  22. ^ a b c Milroy, L. (1980). Language and Social Networks. Oxford: Blackwell.
  23. ^ 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.
  24. ^ Milroy, Leslie and Matthew Gordon (2008).”The Concept of Social Network”. ‘’Sociolinguistics: Method and Interpretation’’. Oxford: John Wiley & Sons. 116-133.
  25. ^ a b Bergs, A. (2005). Social Networks and Historical Sociolinguistics: Studies in Morphosyntactic Variation in the Paston Letters. Berlin: Walter de Gruyter. 22-37.
  26. ^ Trudgill, Peter (2010). Investigations in Sociohistorical Linguistics. Cambridge: University Press. 61-92.
  27. ^ Milroy, L. (1987). Language and Social Networks. New York: Blackwell.
  28. ^ Marshall, Jonathan (2004). Language Change and Sociolinguistics: Rethinking Social Networks (Palgrave Studies in Language Variation). Basingstoke: Palgrave Macmillan.
  29. ^ Labov, William. 1966. The Social Stratification of English in New York City. Washington D.C.: Center for Applied Linguistics.
  30. ^ 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)
  31. ^ Pérez-Sabater, Carmen (2012). "The Linguistics of Social Networking: A Study of Writing Conventions on Facebook". http://www.linguistik-online.de/56_12/perez-sabater.html
  • Trudgill, Peter, ed. (1978). "Belfast: change and variation in an urban vernacular.". Sociolinguistic patterns in British English. London: E.Arnold. pp. 19–36. ISBN 0-7131-5968-5.

Further reading

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