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Detecting Linguistic Diversity on Social Media

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Sidney Wong, Benjamin Adams, Jonathan Dunn

Computation and Language · Submitted 28 Feb 2025

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The results suggest that social media language data has the possibility to provide a rich source of spatial and temporal insights on the linguistic profile of a place.

how it is stated · hedged possibility 1.00 read the record →

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arXiv ID2502.21224 (version 1)
Pith ID5465VSVO, Pith's permanent record of this paper and its review
CitationBenjamin Adams, Jonathan Dunn, Sidney Wong. "Detecting Linguistic Diversity on Social Media." arXiv:2502.21224 (2025). https://pith.science/paper/5465VSVO
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,088 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.23 · human participants 0.10 · controlled comparison 0.57 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.73 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This chapter explores the efficacy of using social media data to examine changing linguistic behaviour of a place. We focus our investigation on Aotearoa New Zealand where official statistics from the census is the only source of language use data. We use published census data as the ground truth and the social media sub-corpus from the Corpus of Global Language Use as our alternative data source. We use place as the common denominator between the two data sources. We identify the language conditions of each tweet in the social media data set and validated our results with two language identification models. We then compare levels of linguistic diversity at national, regional, and local geographies. The results suggest that social media language data has the possibility to provide a rich source of spatial and temporal insights on the linguistic profile of a place. We show that social media is sensitive to demographic and sociopolitical changes within a language and at low-level regional and local geographies.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This chapter explores the efficacy of using social media data to examine changing linguistic behaviour of a place. contribution 0.86 0.54 0.04 asserted 0.48
s1 We focus our investigation on Aotearoa New Zealand where official statistics from the census is the only source of language use data. background 0.58 0.54 0.02 asserted 1.00
s2 We use published census data as the ground truth and the social media sub-corpus from the Corpus of Global Language Use as our alternative data source. method 1.00 0.85 0.02 asserted 0.95
s3 We use place as the common denominator between the two data sources. method 1.00 0.85 0.02 asserted 0.99
s4 We identify the language conditions of each tweet in the social media data set and validated our results with two language identification models. method 1.00 0.96 0.03 asserted 1.00
s5 We then compare levels of linguistic diversity at national, regional, and local geographies. method 0.92 0.91 0.04 asserted 0.71
s6 The results suggest that social media language data has the possibility to provide a rich source of spatial and temporal insights on the linguistic profile of a place. interpretation 0.72 0.88 0.73 hedged possibility 1.00
s7 We show that social media is sensitive to demographic and sociopolitical changes within a language and at low-level regional and local geographies. result 0.91 0.93 0.29 asserted 1.00

What kind of paper the classifier reports this is: empirical study 0.99 · evidence from analysis of existing data 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This chapter explores the efficacy of using social media data to examine changing linguistic behaviour of a place. We focus our investigation on Aotearoa New Zealand where official statistics from the census is the only source of language use data. We use published census data as the ground truth and the social media sub-corpus from the Corpus of Global Language Use as our alternative data source. We use place as the common denominator between the two data sources. We identify the language conditions of each tweet in the social media data set and validated our results with two language identification models. We then compare levels of linguistic diversity at national, regional, and local geographies. The results suggest that social media language data has the possibility to provide a rich source of spatial and temporal insights on the linguistic profile of a place. We show that social media is sensitive to demographic and sociopolitical changes within a language and at low-level regional and local geographies.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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