Understanding U.S. regional linguistic variation with Twitter data analysis

Huang Yuan, Diansheng Guo*, Alice Kasakoff, Jack Grieve

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.
Original languageEnglish
Pages (from-to)244–255
Number of pages12
JournalComputers, Environment and Urban Systems
Early online date31 Dec 2015
Publication statusPublished - Sept 2016

Bibliographical note

© 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/


  • American dialects
  • linguistic
  • regionalization
  • social media
  • spatial data mining
  • Twitter
  • US regions


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