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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Main Authors:	Eisenstein, Jacob, O'Connor, Brendan, Smith, Noah A., Xing, Eric P.
Format:	Online
Language:	English
Published:	Public Library of Science 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4237389/

id	pubmed-4237389
recordtype	oai_dc
spelling	pubmed-42373892014-11-21 Diffusion of Lexical Change in Social Media Eisenstein, Jacob O'Connor, Brendan Smith, Noah A. Xing, Eric P. Research Article 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. Public Library of Science 2014-11-19 /pmc/articles/PMC4237389/ /pubmed/25409166 http://dx.doi.org/10.1371/journal.pone.0113114 Text en © 2014 Eisenstein et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Eisenstein, Jacob O'Connor, Brendan Smith, Noah A. Xing, Eric P.
spellingShingle	Eisenstein, Jacob O'Connor, Brendan Smith, Noah A. Xing, Eric P. Diffusion of Lexical Change in Social Media
author_facet	Eisenstein, Jacob O'Connor, Brendan Smith, Noah A. Xing, Eric P.
author_sort	Eisenstein, Jacob
title	Diffusion of Lexical Change in Social Media
title_short	Diffusion of Lexical Change in Social Media
title_full	Diffusion of Lexical Change in Social Media
title_fullStr	Diffusion of Lexical Change in Social Media
title_full_unstemmed	Diffusion of Lexical Change in Social Media
title_sort	diffusion of lexical change in social media
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.
publisher	Public Library of Science
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4237389/
_version_	1613158676500054016

Diffusion of Lexical Change in Social Media

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