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Article: Language change and social networks

TitleLanguage change and social networks
Authors
KeywordsAgent-based modeling
Language change
Social network
Issue Date2008
PublisherGlobal Science Press. The Journal's web site is located at http://www.global-sci.com/
Citation
Communications In Computational Physics, 2008, v. 3 n. 4, p. 935-949 How to Cite?
AbstractSocial networks play an important role in determining the dynamics and outcome of language change. Early empirical studies only examine small-scale local social networks, and focus on the relationship between the individual speakers' linguistic behaviors and their characteristics in the network. In contrast, computer models can provide an efficient tool to consider large-scale networks with different structures and discuss the long-term effect of individuals' learning and interaction on language change. This paper presents an agent-based computer model which simulates language change as a process of innovation diffusion, to address the threshold problem of language change. In the model, the population is implemented as a network of agents with age differences and different learning abilities, and the population is changing, with new agents born periodically to replace old ones. Four typical types of networks and their effect on the diffusion dynamics are examined. When the functional bias is sufficiently high, innovations always diffuse to the whole population in a linear manner: in regular and small-world networks, but diffuse quickly in a sharp S-curve in random and scale-free networks. The success rate of diffusion is higher in regular and small-world networks than in random and scale-free networks. In addition, the model shows that as long as the population contains a small number of statistical learners who can learn and use both linguistic variants statistically according to the impact of these variants in the input, there is a very high probability for linguistic innovations with only small functional advantage to overcome the threshold of diffusion. © 2008 Global-Science Press.
Persistent Identifierhttp://hdl.handle.net/10722/166998
ISSN
2015 Impact Factor: 1.778
2015 SCImago Journal Rankings: 1.198
References

 

DC FieldValueLanguage
dc.contributor.authorKe, Jen_HK
dc.contributor.authorGong, Ten_HK
dc.contributor.authorWang, WSYen_HK
dc.date.accessioned2012-09-28T02:27:43Z-
dc.date.available2012-09-28T02:27:43Z-
dc.date.issued2008en_HK
dc.identifier.citationCommunications In Computational Physics, 2008, v. 3 n. 4, p. 935-949en_HK
dc.identifier.issn1815-2406en_HK
dc.identifier.urihttp://hdl.handle.net/10722/166998-
dc.description.abstractSocial networks play an important role in determining the dynamics and outcome of language change. Early empirical studies only examine small-scale local social networks, and focus on the relationship between the individual speakers' linguistic behaviors and their characteristics in the network. In contrast, computer models can provide an efficient tool to consider large-scale networks with different structures and discuss the long-term effect of individuals' learning and interaction on language change. This paper presents an agent-based computer model which simulates language change as a process of innovation diffusion, to address the threshold problem of language change. In the model, the population is implemented as a network of agents with age differences and different learning abilities, and the population is changing, with new agents born periodically to replace old ones. Four typical types of networks and their effect on the diffusion dynamics are examined. When the functional bias is sufficiently high, innovations always diffuse to the whole population in a linear manner: in regular and small-world networks, but diffuse quickly in a sharp S-curve in random and scale-free networks. The success rate of diffusion is higher in regular and small-world networks than in random and scale-free networks. In addition, the model shows that as long as the population contains a small number of statistical learners who can learn and use both linguistic variants statistically according to the impact of these variants in the input, there is a very high probability for linguistic innovations with only small functional advantage to overcome the threshold of diffusion. © 2008 Global-Science Press.en_HK
dc.languageengen_US
dc.publisherGlobal Science Press. The Journal's web site is located at http://www.global-sci.com/en_HK
dc.relation.ispartofCommunications in Computational Physicsen_HK
dc.subjectAgent-based modelingen_HK
dc.subjectLanguage changeen_HK
dc.subjectSocial networken_HK
dc.titleLanguage change and social networksen_HK
dc.typeArticleen_HK
dc.identifier.emailGong, T: tgong@hku.hken_HK
dc.identifier.authorityGong, T=rp01654en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-42949169554en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-42949169554&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume3en_HK
dc.identifier.issue4en_HK
dc.identifier.spage935en_HK
dc.identifier.epage949en_HK
dc.publisher.placeHong Kongen_HK
dc.identifier.scopusauthoridKe, J=8944917200en_HK
dc.identifier.scopusauthoridGong, T=35177507200en_HK
dc.identifier.scopusauthoridWang, WSY=35726254300en_HK

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