# When Dialects Collide: How Socioeconomic Mixing Affects Language Use

arXiv: https://arxiv.org/abs/2307.10016 · HTML: https://mlsanity.com/paper/2307.10016/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 72 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift](https://mlsanity.com/paper/1912.01072/) (2019)
- [Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter](https://mlsanity.com/paper/2007.12988/) (2020)
- [Dialectometric analysis of language variation in Twitter](https://mlsanity.com/paper/1702.06777/) (2017)
- [Crowdsourcing Dialect Characterization through Twitter](https://mlsanity.com/paper/1407.7094/) (2014)
- [Socioeconomic Dependencies of Linguistic Patterns in Twitter: A Multivariate Analysis](https://mlsanity.com/paper/1804.01155/) (2018)
- [Confounds and Consequences in Geotagged Twitter Data](https://mlsanity.com/paper/1506.02275/) (2015)
- [Mapping the Americanization of English in Space and Time](https://mlsanity.com/paper/1707.00781/) (2017)
- [Race, Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States](https://mlsanity.com/paper/1605.02951/) (2016)
- [Capturing the diversity of multilingual societies](https://mlsanity.com/paper/2105.02570/) (2021)
- [American cultural regions mapped through the lexical analysis of social media](https://mlsanity.com/paper/2208.07649/) (2022)
- [Semantic homophily in online communication: evidence from Twitter](https://mlsanity.com/paper/1606.08207/) (2016)

## Built on by (most cited first, 2 of 2)

- [Evolving linguistic divergence on polarizing social media](https://mlsanity.com/paper/2309.01659/) (2023)
- [Entropy and type-token ratio in gigaword corpora](https://mlsanity.com/paper/2411.10227/) (2024)

## Abstract (arXiv)

The socioeconomic background of people and how they use standard forms of language are not independent, as demonstrated in various sociolinguistic studies. However, the extent to which these correlations may be influenced by the mixing of people from different socioeconomic classes remains relatively unexplored from a quantitative perspective. In this work we leverage geotagged tweets and transferable computational methods to map deviations from standard English on a large scale, in seven thousand administrative areas of England and Wales. We combine these data with high-resolution income maps to assign a proxy socioeconomic indicator to home-located users. Strikingly, across eight metropolitan areas we find a consistent pattern suggesting that the more different socioeconomic classes mix, the less interdependent the frequency of their departures from standard grammar and their income become. Further, we propose an agent-based model of linguistic variety adoption that sheds light on the mechanisms that produce the observations seen in the data.