# Assessing Digital Language Support on a Global Scale

arXiv: https://arxiv.org/abs/2209.13515 · HTML: https://mlsanity.com/paper/2209.13515/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 27 Sep 2022.

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

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities](https://mlsanity.com/paper/1912.03457/) (2019)

## Built on by (most cited first, 6 of 6)

- [The Zeno's Paradox of `Low-Resource' Languages](https://mlsanity.com/paper/2410.20817/) (2024)
- [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://mlsanity.com/paper/2311.05552/) (2023)
- [Invisible Languages of the LLM Universe](https://mlsanity.com/paper/2510.11557/) (2025)
- [CebuaNER: A New Baseline Cebuano Named Entity Recognition Model](https://mlsanity.com/paper/2310.00679/) (2023)
- [M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models](https://mlsanity.com/paper/2608.03803/) (2026)
- [UzWordnet and Generative AI for Learning Uzbek by Game Playing](https://mlsanity.com/paper/2607.14104/) (2026)

## Abstract (arXiv)

The users of endangered languages struggle to thrive in a digitally-mediated world. We have developed an automated method for assessing how well every language recognized by ISO 639 is faring in terms of digital language support. The assessment is based on scraping the names of supported languages from the websites of 143 digital tools selected to represent a full range of ways that digital technology can support languages. The method uses Mokken scale analysis to produce an explainable model for quantifying digital language support and monitoring it on a global scale.