When Dialects Collide: How Socioeconomic Mixing Affects Language Use

TL;DR

This study investigates how socioeconomic mixing influences language use, revealing that greater mixing reduces the correlation between income levels and deviations from standard English, using large-scale geotagged Twitter data and modeling.

Contribution

It introduces a large-scale quantitative analysis of socioeconomic mixing effects on language, supported by an agent-based model explaining the observed patterns.

Findings

Greater socioeconomic mixing correlates with decreased dependence of language deviations on income.

Large-scale geotagged Twitter data effectively maps language variation across regions.

Agent-based modeling provides mechanistic insights into language adoption dynamics.

Abstract

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.