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From sunblock to softblock: Analyzing the correlates of neology in published writing and on social media

Summary

The paper asks whether the semantic factors linked to the birth of new English words (neology) are the same on social media as in edited print. Earlier work by the same group, based mostly on historical publications, proposed two explanations. The supply hypothesis says new words tend to fill sparse regions of meaning space. The demand hypothesis says they appear around topics whose vocabulary is gaining frequency. The authors built a corpus of about 260M English tweets and revisited COHA/COCA published texts. Each neologism was compared with a matched non-neologism control word, using neighbourhood statistics from both static and contextual (RoBERTa) embeddings. The original print findings were reproduced, and Twitter showed broadly similar patterns. However, the popularity-growth signal was weaker on Twitter. The authors attribute this partly to fewer time steps and partly to Twitter favouring playful coinage, such as respellings, blends and abbreviations, over naming new concepts. The study is presented as the first look at semantic drivers of word emergence on social media.

Key points

  • Twitter corpus: about 260M English tweets (2007–2021), with up to 100K tweets sampled per day and retweets and bots removed. Tweets from 2007–2010 form the HISTORICAL split and 2011–2021 the MODERN split.
  • Published-writing data: COHA (1800–1989) and COCA (1990–2012), limited to fiction, non-fiction, academic and press texts. The study reuses an earlier list of 1000 noun neologisms.
  • Results: both the supply and demand hypotheses hold in print and on Twitter. On Twitter the demand evidence is less conclusive. The frequency-monotonicity measure is too noisy there, since Twitter has only 4 yearly steps versus 18 decades in print.
  • Embedding choice: RoBERTa neighbourhoods are distorted by subword overlap. For example, smol ends up near other sm- spellings rather than cute. This makes contextual embeddings poorly suited to social-media neologisms.
  • Resources: code, word lists and tweet IDs are released at github.com/ryskina/twitter-neology.

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

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.lchange-1.14

@inproceedings{ryskina2026sunblock,
  title = {{From sunblock to softblock: Analyzing the correlates of neology in published writing and on social media}},
  author = {Ryskina, Maria and Gormley, Matthew R. and Mahowald, Kyle and Mortensen, David R. and Berg-Kirkpatrick, Taylor and Kulkarni, Vivek},
  booktitle = {The Proceedings for the 6th International Workshop on Computational Approaches to Language Change (LChange'26)},
  year = {2026},
  doi = {10.18653/v1/2026.lchange-1.14},
  url = {https://doi.org/10.18653/v1/2026.lchange-1.14}
}

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