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Mapping the Americanization of English in Space and Time

Paper · 2017 · Bruno Gonçalves, Lucia Loureiro-Porto, Jose J. Ramasco et al.

Summary

The paper asks how far American English has displaced British norms worldwide and when this shift took hold. The authors built two lists of British/American pairs. One covers vocabulary, such as lorry/truck and nappy/diaper. The other covers spelling, such as colour/color and centre/center. Each pair was kept only if one form was clearly more frequent in either the BNC or COCA. They then measured which member of each pair appears in two very different sources. The spatial view uses about 213 million geolocated English tweets from the Twitter Decahose. The historical view uses the British and American Google Books corpora for 1800–2010. A signed polarization ratio places each country or year on a scale from British to American usage. American forms prevail almost everywhere outside the UK and are spreading inside it too. British conventions hold mainly in former colonies with strong ties to British norms. Over time, the spelling split begins around Webster's 1828 dictionary. The work shows that informal and formal corpora give the same broad picture of a dialect shift.

Key points

  • The Twitter data are English geotagged tweets from May 10, 2010 to Feb 28, 2016, with language identified by the Chromium Compact Language Detection library.
  • Yearly Google Books corpora range from 18.4M words (US, 1800) to 8.2B words (US, 2000; UK 2.2B).
  • Candidate pairs came from reference books and the Oxford Dictionaries usage pages, then were filtered by a significant frequency skew in BYU-BNC versus COCA.
  • Spelling divergence: it grows over two centuries from about 1828, while vocabulary follows the same trend more weakly.
  • Shifts over time:
    • American books lean more British after WWII until after 1960, which the authors link to European immigration.
    • British books turn toward American forms in the last two decades of the data, after the Cold War.

Interesting ideas

  • I1. Build contrast lists of concept-equivalent pairs and keep only those whose skew is confirmed in two reference corpora, so that each item is a validated marker rather than a dictionary guess.
  • I2. Collapse many weak per-word signals into a single signed score per region or year, which makes drift easy to read and plot.
  • I3. Use two sources with very different registers and trust a conclusion only when both agree, since each has its own bias.
  • I4. Treat spelling and vocabulary as separate dimensions, because they change at different speeds (spelling settles faster than new words spread).
  • I5. An explicit pair inventory makes it possible to detect a text's variety and stay consistent with it.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (tokens): A pair table like colour/color or analyse/analyze could map both spellings to one lemma while recording a dialect feature on the token, keeping the choice explainable.
  • I2 (learn): A similar signed score could track which variety dominates the silver training data and whether parser accuracy differs on American- versus British-leaning texts.
  • I3 (learn): Gains in a repair rule or parser could be required to hold on both formal and informal text before acceptance, which works like a cross-register sanity check.
  • I4 (world): The ontology could store lexical alternations such as lorry/truck as one concept with variety-tagged names, kept apart from mere orthographic variants.
  • I5 (back): The generator could detect the input's variety and produce output in the same variety, choosing words and spellings from the pair table.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓New York UniversityNew York · United States136 works · 86,390 citations

Also on the byline: Universitat de les Illes Balears · Institute for Cross-Disciplinary Physics and Complex Systems.

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 1Research subjects and languages the work is about.English · 1,373 works

Cite

Cite as Bruno Gonçalves, Lucia Loureiro-Porto, Jose J. Ramasco and David Sanchez. 2017. Mapping the Americanization of English in Space and Time. arXiv:1707.00781 · PLoS ONE 13: e0197741 (2018).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1371/journal.pone.0197741 · arXiv 1707.00781

@misc{gonccalves2017mapping,
  title = {{Mapping the Americanization of English in Space and Time}},
  author = {Bruno Gonccalves, Lucia Loureiro-Porto, Jose J. Ramasco and David Sanchez},
  year = {2017},
  doi = {10.1371/journal.pone.0197741},
  eprint = {1707.00781},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1371/journal.pone.0197741}
}

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