Diffusion of Lexical Change in Social Media

Article, Preprint English OPEN

Jacob Eisenstein; Brendan O'Connor; Noah A Smith; Eric P Xing;

(2012)

Publisher: Public Library of Science

Journal: PLoS ONE,volume 9,issue 11 (issn: 1932-6203, eissn: 1932-6203)

Publisher copyright policies & self-archiving

Related identifiers: doi: 10.1371/journal.pone.0113114, pmc: PMC4237389
Subject: Linguistics | Research Article | Mathematics | Computational Linguistics | Mathematical and Statistical Techniques | Artificial Intelligence | Monte Carlo method | Computer Science - Computation and Language | Physical sciences | Linguistic Geography | Statistics (mathematics) | Biology and Life Sciences | Computer and Information Sciences | Computer Science - Social and Information Networks | Neuroscience | Physics - Physics and Society | Research and Analysis Methods | Medicine | Machine Learning | Q | R | Social Sciences | Science | Dialectology | Statistical methods | Sociolinguistics | Cognitive Science

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 vec... View more

References (63)
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1 Androutsopoulos JK (2000) Non-standard spellings in media texts: The case of German fanzines. Journal of Sociolinguistics 4: 514–533.
2 Anis J (2007) Neography: Unconventional spelling in French SMS text messages. In: Danet B, Herring SC, editors, The Multilingual Internet: Language, Culture, and Communication Online, Oxford University Press. pp. 87–115.
3 Herring SC (2012) Grammar and electronic communication. In: Chapelle CA, editor, The Encyclopedia of Applied Linguistics, Wiley.
4 Crystal D (2006) Language and the Internet. Cambridge University Press, second edition.
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6 Thurlow C (2006) From statistical panic to moral panic: The metadiscursive construction and popular exaggeration of new media language in the print media. Journal of Computer-Mediated Communication 11: 667–701.
7 Eisenstein J, O'Connor B, Smith NA, Xing EP (2010) A latent variable model for geographic lexical variation. In: Proceedings of Empirical Methods for Natural Language Processing (EMNLP). pp. 1277–1287.
8 Eisenstein J, Smith NA, Xing EP (2011) Discovering sociolinguistic associations with structured sparsity. In: Proceedings of the Association for Computational Linguistics (ACL). pp. 1365–1374.
9 Schwartz HA, Eichstaedt JC, Kern ML, Dziurzynski L, Ramones SM, et al (2013) Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS ONE 8: e73791.24086296
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Related Research Results (11)

Inferred by OpenAIRE

11 research results, page 1 of 2

dataset Average accuracy predicting links between MSA pairs, and its Monte Carlo standard error (calculated from simulation samples). (2014)	73%
dataset Differences between linked and (sampled) non-linked pairs of cities, summarized by their mean and its standard error. (2014)	73%
dataset Statistics of metropolitan statistical areas. (2014)	73%
dataset Table of mathematical notation. (2014)	73%
dataset Diffusion of Lexical Change in Social Media (2014)	73%
software tweetmotif software on GitHub	72%
dataset The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place (2013)	71%
other Beyond Word Frequency: Bursts, Lulls, and Scaling in the Temporal Distributions of Words (2009)	64%
other Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter (2011)	63%
other Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013)	61%

Related Organizations (6)

Georgia Institute of Technology ( Georgia Tech ) United States Website url: http://www.gatech.edu/	90%
Carnegie Mellon University ( CMU ) United States Website url: http://www.cmu.edu/index.shtml	90%
CARNEGIE-MELLON UNIVERSITY	90%
GEORGIA INSTITUTE OF TECHNOLOGY	90%
UNIVERSITY OF MASSACHUSETTS	90%
School of Computer Science Carnegie-Mellon University United States	90%

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Funded by
NSF| CAREER: Flexible Learning for Natural Language Processing
NSF| Collaborative Research: Discovering and Exploiting Latent Communities in Social Media
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