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Evolving linguistic divergence on polarizing social media

Paper · 2023 · Andres Karjus and Christine Cuskley

Summary

The paper asks whether everyday US English is drifting apart along the left–right political divide, and how to measure such drift in ordinary users rather than politicians. The authors propose a way to label social media users by political leaning. A user counts as left- or right-leaning if they follow news outlets of one bias category and none from the other. Applying this to Twitter in 2021, they mined the follower lists of 72 major news accounts. From these they built a corpus of about 1.5M posts by roughly 11k US users. They compared the two subcorpora on four aspects: conversation topics, word and emoji frequencies, sentiment, and lexical meaning. To do so, they drew on statistical analysis of vocabulary, machine learning methods and large language models, together with a systematic human annotation exercise. Divergence appears on every dimension, most clearly in what people talk about, with some signs of shifting word meanings. Overall the language remains mutually intelligible, but the results point to spots where misunderstanding could grow as polarization deepens. The pipeline does not depend on a particular language or platform.

Key points

  • The corpus has 1,483,385 tweets, about 20M words, from 10,986 unique US users, collected February–September 2021.
  • To build it, the authors gathered follower lists totalling 422M entries for 72 large news media accounts.
  • Users were included only if they met all of these conditions:
    • they followed outlets from just one bias category;
    • they self-reported a US location;
    • their account existed by February 2021;
    • they posted at least 10 times in the observation window;
    • they showed some engagement with other users.
  • The analyses covered topics, frequencies of words and emoji, sentiment, and lexical semantics. Machine-derived meaning findings were cross-checked through human annotation of a subset of words and emoji.
  • The code is public (github.com/andreskarjus/evolving_divergence). The tweet data and IDs are withheld because of platform Terms of Service, but are available on request.

Interesting ideas

  • I1. Users are assigned to a group by what they follow and also by what they avoid following, which yields cleaner contrast groups than inclusion alone; the authors note this generalizes to subreddits, Wikipedia editing domains, or film festivals.
  • I2. Meaning differences estimated by models are checked against a small, systematic human annotation exercise, rather than trusting large correlational data on its own.
  • I3. "Dog whistles" are words with an ordinary public reading plus a hidden reading meant for one subgroup, e.g. "inner city" or the "okay" hand sign.
  • I4. Emoji are analysed as full lexical items with their own frequencies and meanings, and those meanings can diverge between communities just like words.
  • I5. Word meaning is treated as community-relative: the same form can carry partially different semantics in two groups that share one language.

For Mova

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

  • I1 (learn): Silver training data could be split into contrastive pools by source characteristics that are present in one pool and absent in the other, so that register- or genre-specific parser errors become visible.
  • I2 (learn): Each automatically induced finding (a repair rule, a lexical sense shift) could come with a compact human-checked probe set acting as a sanity control.
  • I3 (frames): The implicature module could represent audience-dependent secondary meanings explicitly, flagging that an utterance's force may differ depending on which community is reading it.
  • I4 (tokens): The tokenizer and lemma layer could treat emoji as proper lexemes with sense entries, instead of discarding them as noise.
  • I5 (world): Lexicon and ontology entries could carry an optional community or register tag, so that the world-of-the-text builder picks the sense that fits the source.

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

🎓Newcastle UniversityNewcastle upon Tyne · United Kingdom19 works · 746 citations🎓Estonian Business SchoolTallinn · Estonia2 works · 59 citations🎓Tallinn UniversityTallinn · Estonia2 works · 59 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksSocial Sciences · 1,954 worksGeneral Social Sciences · 254 worksCommunication · 104 works

Cite

Cite as Andres Karjus and Christine Cuskley. 2023. Evolving linguistic divergence on polarizing social media. arXiv:2309.01659.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2309.01659 · arXiv 2309.01659

@misc{karjus2023evolving,
  title = {{Evolving linguistic divergence on polarizing social media}},
  author = {Andres Karjus and Christine Cuskley},
  year = {2023},
  doi = {10.48550/arxiv.2309.01659},
  eprint = {2309.01659},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2309.01659}
}

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