---
title: "Characterizing English Variation across Social Media"
description: "Earlier studies looked mainly at which words a group uses. 67 references, cited by 21 papers here."
authors: ["Li Lucy", "David Bamman"]
year: 2021
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2021.tacl-1.33/"
doi: "10.1162/tacl_a_00383"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lucy-2021-characterizing-english-variation-across-social/"
---
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# Characterizing English Variation across Social Media Communities with BERT

**Authors:** [Li Lucy](https://md.research.elamur.ai/free/en/authors/li-lucy/) and [David Bamman](https://md.research.elamur.ai/free/en/authors/david-bamman/) · **Year:** 2021 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2021](https://md.research.elamur.ai/free/en/venues/tacl/2021/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.tacl-1.33/) · [PDF](https://aclanthology.org/2021.tacl-1.33.pdf) · [DOI 10.1162/tacl\_a\_00383](https://doi.org/10.1162/tacl_a_00383) · [OpenAlex](https://openalex.org/W3169432135)

**Citations:** 36 citations (OpenAlex, 2026-10-14) · [21](https://md.research.elamur.ai/free/en/papers/lucy-2021-characterizing-english-variation-across-social/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks how to measure how far an online community's English departs from general usage. Earlier studies looked mainly at which words a group uses. This one also asks whether a group uses familiar words in its own way. The authors sampled two months of Reddit comments (May–June 2019) from 474 mostly-English subreddits and scored community-specific language along two axes:
- **Type-based:** PMI-style specificity of word types, plus tf-idf.
- **Sense-based:** contextual meaning variation measured with BERT, using sense clusters built from substitutes that BERT predicts for masked target words. This approach was first checked on SemEval WSI benchmarks.

To validate the scores, they collected glossaries that users wrote for 57 subreddits. Words with community-specific senses tend to appear in those glossaries. Regressions then link each community's share of distinctive words to social traits. Communities with the most distinctive language are mid-sized and have active, loyal users. User behavior predicts distinctiveness better than topic does. The work shows that contextual embeddings can turn sociolinguistic hypotheses into large-scale measurements.

## Key points
- Data: the 500 busiest subreddits, filtered with Lui and Baldwin's language identifier to those with at least 85% English comments. This left 474 communities, each down-sampled to 80,000 comments, totalling over 1.4 billion tokens and about 20k users per subreddit.
- A word's frequency in a community is the number of distinct users who used it, following Eisenstein et al. (2014). The type analysis keeps only the top 20% most frequent words of each subreddit.
- Released resource: glossaries for 57 subreddits, averaging 72.4 terms and ranging from 4 to 251. After 1044 multi-word expressions were removed, 2814 single-token entries remained, covering 2226 unique words. Code is at github.com/lucy3/ingroup_lang.
- Among the type metrics, type NPMI worked best. Example high-scoring words are *jnmom* in r/justnomil and *tlou* in r/ps4.
- OLS on the community score F reached R² 0.505 with user-based features alone and 0.529 once a topic indicator was added. Community size and network density had negative coefficients, while user activity and loyalty had positive ones.

## Interesting ideas
- Counting a word by how many distinct users produce it, rather than by raw tokens, prevents a few prolific posters from inflating a term's apparent importance.
- Senses are induced by masking a word, collecting the model's predicted substitutes, and clustering those substitute sets, so every sense cluster comes with a readable list of replacement words.
- An unsupervised distinctiveness score is checked against glossaries that communities wrote themselves, which is cheap, naturally occurring gold data.
- The same regression is fitted with and without a topic variable to see whether topic explains away the effect of user behavior, a simple way to expose confounds.
- Multi-word expressions were dropped so that "which words are used" and "how a word is used" stay cleanly separated, avoiding a mix of lexical and semantic signals.

## For Mova

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

- **I1** (learn): when mining self-induced repair rules or silver agreements, count supporting distinct documents or sources instead of raw hits, so one repetitive text cannot create a rule.
- **I2** (world): substitute lists could serve as explainable evidence when linking an ambiguous token to a WordNet synset or flagging a meaning missing from the ontology.
- **I3** (learn): user-written domain glossaries (school subject vocabularies, game wikis) could test whether Mova's detector for novel or domain-specific lexicon finds the right words.
- **I4** (learn): evaluation could pair each accuracy claim with a control model that adds a plausible confound (genre, text length) to check whether the claimed factor survives.
- **I5** (tokens): keeping multiword-unit detection as a layer separate from single-token lemma and sense assignment would make errors traceable to one level instead of a blend.

*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

- [University of California, Berkeley](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/) — University, Berkeley, United States · 175 works · 227,440 citations

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🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works

## Cite

Cite as: Li Lucy and David Bamman. 2021. Characterizing English Variation across Social Media Communities with BERT. Transactions of the Association for Computational Linguistics.

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

```bibtex
@article{lucy2021characterizing,
  title = {{Characterizing English Variation across Social Media Communities with BERT}},
  author = {Li Lucy and David Bamman},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2021},
  doi = {10.1162/tacl\_a\_00383},
  url = {https://doi.org/10.1162/tacl_a_00383}
}
```

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