Diffusion of lexical change in social media
Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to ag...
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| Veröffentlicht in: | PloS one 2014-11, Vol.9 (11), p.e113114 |
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| creator | Eisenstein, Jacob O'Connor, Brendan Smith, Noah A Xing, Eric P |
| description | Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity - especially with regard to race - plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English. |
| doi_str_mv | 10.1371/journal.pone.0113114 |
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We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity - especially with regard to race - plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0113114</identifier><identifier>PMID: 25409166</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>American English ; Autoregressive models ; Autoregressive processes ; Biology and Life Sciences ; Changes ; Communication ; Computer and Information Sciences ; Computer mediated communication ; Computer science ; Demographics ; Demography ; Dialects ; Diffusion ; Digital media ; Ethnicity ; Fault lines ; Geography ; Geological faults ; Human communication ; Humans ; Language ; Language change ; Linguistics ; Local elections ; Models, Statistical ; Origin of language ; Phonetics ; Physical sciences ; Population (statistical) ; Population number ; Quantitative analysis ; Research and Analysis Methods ; Researchers ; Sampling ; Semantic change ; Social interactions ; Social Media ; Social networks ; Social Sciences ; Statistical analysis ; Technological change ; Terminology as Topic ; Time series ; United States - ethnology ; Vocabulary</subject><ispartof>PloS one, 2014-11, Vol.9 (11), p.e113114</ispartof><rights>2014 Eisenstein et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity - especially with regard to race - plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Eisenstein, Jacob</au><au>O'Connor, Brendan</au><au>Smith, Noah A</au><au>Xing, Eric P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diffusion of lexical change in social media</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2014-11-19</date><risdate>2014</risdate><volume>9</volume><issue>11</issue><spage>e113114</spage><pages>e113114-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity - especially with regard to race - plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. 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| subjects | American English Autoregressive models Autoregressive processes Biology and Life Sciences Changes Communication Computer and Information Sciences Computer mediated communication Computer science Demographics Demography Dialects Diffusion Digital media Ethnicity Fault lines Geography Geological faults Human communication Humans Language Language change Linguistics Local elections Models, Statistical Origin of language Phonetics Physical sciences Population (statistical) Population number Quantitative analysis Research and Analysis Methods Researchers Sampling Semantic change Social interactions Social Media Social networks Social Sciences Statistical analysis Technological change Terminology as Topic Time series United States - ethnology Vocabulary |
| title | Diffusion of lexical change in social media |
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