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Computational Sociolinguistics: A Survey

Paper · 2016 · Dong Nguyen, A. Seza Doğruöz, Carolyn P. Rosé et al.

Summary

This survey maps the emerging research area that the authors call computational sociolinguistics, where computational linguistics (CL) meets sociolinguistics. Their starting point is that CL has mainly modelled what language conveys informationally. Its social side has received much less attention: how speakers signal identity, manage relationships and mix languages. Social media data has made that side newly measurable. The authors argue that speaker agency loosens the link between social categories and language. People can choose how to sound, so traits like gender are not fixed predictors of style. The article reviews CL work on three areas:

  • language and social identity (gender, age, geography)
  • social interaction (relationships, style-shifting, community norms, language change)
  • multilingual communication (language identification, parsing, code-mixing patterns)

The survey is not an experiment. Its contribution is a synthesis and a research agenda. Large-scale data can extend small, carefully designed sociolinguistic studies. In turn, sociolinguistics can challenge CL's static categories and its narrow focus on benchmark scores. This matters for building fairer NLP tools that handle varied language.

Key points

  • Published in Computational Linguistics (2016) by Nguyen, Doğruöz, Rosé and de Jong (Twente, Tilburg/NIAS, CMU, Erasmus); doi:10.1162/COLI_a_00258.
  • Corpus bias: Hovy and Søgaard (2015) found that biases in the Wall Street Journal corpus make NLP tools work better on text by older authors. Jørgensen, Hovy and Søgaard (2015) report that POS taggers do worse on African American Vernacular English.
  • Gender classification: in a blog corpus, Gianfortoni et al. (2011) found that common n-gram features for gender lost their power once occupation was controlled for. Pattern-based features grounded in sociolinguistic findings still worked.
  • Interpretable models: sparse models such as Eisenstein, Smith and Xing (2011) suit exploratory analysis better than models with thousands of features. However, interpretable models may trade away some accuracy (Piergallini et al. 2014).
  • Text normalization: following Eisenstein (2013b), the authors warn that normalizing informal text removes the very variation sociolinguists study. They call for pre-processing tools that handle variation without social-group bias.

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

🎓Carnegie Mellon UniversityPittsburgh · United States365 works · 158,048 citations🎓Tilburg UniversityTilburg · Netherlands40 works · 6,231 citations🎓University of TwenteEnschede · Netherlands4 works · 415 citations🎓Erasmus University RotterdamRotterdam · Netherlands2 works · 288 citations

Also on the byline: Netherlands Institute for Advanced Study in the Humanities and Social Sciences.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

Cite

Cite as Dong Nguyen, A. Seza Doğruöz, Carolyn P. Rosé and Franciska de Jong. 2016. Computational Sociolinguistics: A Survey. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00258

@article{nguyen2016computational,
  title = {{Computational Sociolinguistics: A Survey}},
  author = {Dong Nguyen, A. Seza Doğruöz, Carolyn P. Rosé and Franciska {de Jong}},
  journal = {Computational Linguistics},
  year = {2016},
  doi = {10.1162/coli\_a\_00258},
  url = {https://doi.org/10.1162/coli_a_00258}
}

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