# Comparing Measures of Linguistic Diversity Across Social Media Language Data and Census Data at Subnational Geographic Areas

arXiv: https://arxiv.org/abs/2308.10452 · HTML: https://mlsanity.com/paper/2308.10452/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Mapping Languages: The Corpus of Global Language Use](https://mlsanity.com/paper/2004.00798/) (2020)
- [Computational Sociolinguistics: A Survey](https://mlsanity.com/paper/1508.07544/) (2015)
- [Mapping Languages and Demographics with Georeferenced Corpora](https://mlsanity.com/paper/2004.00809/) (2020)
- [Language Identification for Austronesian Languages](https://mlsanity.com/paper/2206.04327/) (2022)

## Built on by (most cited first, 1 of 1)

- [cantnlp@LT-EDI-2024: Automatic Detection of Anti-LGBTQ+ Hate Speech in Under-resourced Languages](https://mlsanity.com/paper/2401.15777/) (2024)

## Abstract (arXiv)

This paper describes a preliminary study on the comparative linguistic ecology of online spaces (i.e., social media language data) and real-world spaces in Aotearoa New Zealand (i.e., subnational administrative areas). We compare measures of linguistic diversity between these different spaces and discuss how social media users align with real-world populations. The results from the current study suggests that there is potential to use online social media language data to observe spatial and temporal changes in linguistic diversity at subnational geographic areas; however, further work is required to understand how well social media represents real-world behaviour.