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Syntactic Geolectal Variation: Traditional approaches, current challenges and new tools
Edited by Alba Cerrudo, Ángel J. Gallego and Francesc Roca Urgell
[Issues in Hispanic and Lusophone Linguistics 34] 2021
► pp. 347–380

Chapter 12
Using Twitter to build a corpus for linguistic variation

Collecting tweets and mapping them out

| Sophia University

Data for linguistic analysis from social media are being used for a variety of reasons. One of the most attractive reasons is the possibility of gathering very large quantities of data in the digital form. However, collecting data from these sources is not always a simple task. In this chapter, a few methods to harvest data from Twitter will be introduced like FireAnt, 140dev Streaming API Framework, and Elastic Stack, as well as visualization tools like QGIS to map geotagged tweets. Finally, some examples of tweet analyses are discussed.

Keywords: linguistic variation, data mining, geocorpus, visualization, social media, Twitter
Article outline
  • 1.Introduction
  • 2. FireAnt
  • 3. 140dev Streaming API Framework
  • 4.Elastic Stack
  • 5. QGIS
  • 5.Conclusions
  • Notes
  • References
Published online: 26 November 2021
DOI logo
https://doi.org/10.1075/ihll.34.12tin
References
Anthony, L., & Hardaker, C.
(2017) FireAnt (Version 1.1.4) [Computer Software]. Tokyo, Japan: Waseda University. [[URL]]
Azarmi, B.
(2017) Learning Kibana 5.0. Birmingham, UK: Packt Publishing.Google Scholar
Dixit, B.
(2017) Mastering Elasticsearch 5.x. (3rd ed.). Birmingham, UK: Packt Publishing.Google Scholar
Dyer, R. J. T.
(2015) Learning MySQL and MariaDB: Heading in the right direction with MySQL and MariaDB (1st ed.). Sebastopol, CA: O’Reilly Media.Google Scholar
Graser, A.
(2016a) Learning QGIS (3rd ed.). Birmingham, UK: Packt Publishing.Google Scholar
(2016b) Map design paperback. Birmingham, UK: Packt Publishing.Google Scholar
Gupta, Y., & Gupta, R. V.
(2017) Mastering Elastic Stack. Birmingham, UK: Packt Publishing.Google Scholar
Lockhart, J.
(2015) Modern PHP: New features and good practices (1st ed.). Sebastopol, CA: O’Reilly Media.Google Scholar
Russell, M. A., & Klassen, M.
(2014) Mining the social web: Data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more (3rd ed.). Sebastopol, CA: O’Reilly Media.Google Scholar
Sklar, D.
(2016) Learning PHP: A gentle introduction to the web’s most popular language (1st ed.). Sebastopol, CA: O’Reilly Media.Google Scholar
Cited by

Cited by 1 other publications

OUSHIRO, LIVIA
2023. Computational Resources for Handling Sociolinguistic Corpora. In The Handbook of Usage‐Based Linguistics, ► pp. 415 ff. DOI logo

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