Journal article
Understanding U.S. regional linguistic variation with Twitter data analysis
Computers, environment and urban systems, Vol.59, pp.244-255
09/2016
DOI: 10.1016/j.compenvurbsys.2015.12.003
Abstract
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.
•This paper seeks to understand U.S. regional linguistic variation by analyzing big data of geo-tagged tweets.•This paper applies a hierarchical regionalization method to discover hierarchical dialect regions.•This paper is among the very few papers that use social media data to study nation-wide linguistic variations.
Details
- Title: Subtitle
- Understanding U.S. regional linguistic variation with Twitter data analysis
- Creators
- Yuan Huang - University of South CarolinaDiansheng Guo - University of South CarolinaAlice Kasakoff - University of South CarolinaJack Grieve - Aston University
- Resource Type
- Journal article
- Publication Details
- Computers, environment and urban systems, Vol.59, pp.244-255
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.compenvurbsys.2015.12.003
- ISSN
- 0198-9715
- eISSN
- 1873-7587
- Grant note
- DOI: 10.13039/100000208, name: Institute of Museum and Library Services; DOI: 10.13039/100000001, name: National Science Foundation, award: 0748813
- Language
- English
- Date published
- 09/2016
- Academic Unit
- Biostatistics
- Record Identifier
- 9984363609302771
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