# Crowdsourcing Dialect Characterization through Twitter

arXiv: https://arxiv.org/abs/1407.7094 · HTML: https://mlsanity.com/paper/1407.7094/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2014.

## Built on by (most cited first, 11 of 11)

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- [Regionalized models for Spanish language variations based on Twitter](https://mlsanity.com/paper/2110.06128/) (2021)
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- [American cultural regions mapped through the lexical analysis of social media](https://mlsanity.com/paper/2208.07649/) (2022)
- [When Dialects Collide: How Socioeconomic Mixing Affects Language Use](https://mlsanity.com/paper/2307.10016/) (2023)
- [Syntactic Variation Across the Grammar: Modelling a Complex Adaptive System](https://mlsanity.com/paper/2309.11869/) (2023)
- [Digital Linguistic Bias in Spanish: Evidence from Lexical Variation in LLMs](https://mlsanity.com/paper/2602.09346/) (2026)
- [Measuring Linguistic Diversity During COVID-19](https://mlsanity.com/paper/2104.01290/) (2021)
- [Detecting Historical Turning Points in Italian Media: A Complex Systems Approach to a Diachronic News Corpus](https://mlsanity.com/paper/2606.14348/) (2026)

## Abstract (arXiv)

We perform a large-scale analysis of language diatopic variation using geotagged microblogging datasets. By collecting all Twitter messages written in Spanish over more than two years, we build a corpus from which a carefully selected list of concepts allows us to characterize Spanish varieties on a global scale. A cluster analysis proves the existence of well defined macroregions sharing common lexical properties. Remarkably enough, we find that Spanish language is split into two superdialects, namely, an urban speech used across major American and Spanish citites and a diverse form that encompasses rural areas and small towns. The latter can be further clustered into smaller varieties with a stronger regional character.