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Review published In:
Register Studies
Vol. 2:1 (2020) ► pp.166–171
Book review

Biber, D. & Egbert, J. (2018). Register variation online

Reviewed by
| University of North Texas
Article outline
  • 1.Introduction
  • 2.Summary of chapters
  • 3.Evaluation
    • 3.1Methodological contribution 1: Using Mechanical Turk to recruit end users to classify web texts
    • 3.2Methodological contribution 2: Developing new keyword and key feature methodologies
    • 3.3Implication for future studies of internet language
  • References
Published online: 10 April 2020
DOI logo
https://doi.org/10.1075/rs.19018.smi
References
Biber, D., & Egbert, J.
(2016) Register variation on the searchable web: A multi-dimensional analysis. Journal of English Linguistics, 44(2), 95–137. DOI logoGoogle Scholar
Biber, D., Egbert, J., & Davies, M.
(2015) Exploring the composition of the searchable web: A corpus-based taxonomy of web registers. Corpora, 10(1), 11–45. DOI logoGoogle Scholar
Chris
(2019, November 18). Writer who never learned to drive works for Uber. Makes $0.97/hr. [Blog post]. Retrieved from: [URL]> (3 December 2019).
Davies, M.
(2013) Corpus of Global Web-Based English: 1.9 billion words from speakers in 20 countries (GloWbE). Retrieved from: [URL]> (3 December 2019).
Egbert, J., & Biber, D.
(2019) Incorporating text dispersion into keyword analyses. Corpora, 14(1), 77–104.
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Egbert, J., Biber, D., & Davies, M.
(2015) Developing a bottom-up, user-based method of web register classification. Journal of the Association for Information Science and Technology, 66(9), 1817–1831. DOI logoGoogle Scholar
Newman, A.
(2019, November 15). I found work on an Amazon website. I made 97 cents an hour. The New York Times. Retrieved from: [URL]> (3 December 2019).