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Updated: Sep 15, 2025

Examining Bilingual Language Control Using the Stroop Task
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When dialects collide: how socioeconomic mixing affects language use.

Thomas Louf1,2, José J Ramasco1, David Sánchez1

  • 1Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC), Palma de Mallorca, Spain.

EPJ Data Science
|July 14, 2025
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Summary

Socioeconomic status and language use are linked, but social mixing weakens this connection. As different socioeconomic groups interact more, their language variations become less dependent on income.

Keywords:
Agent-based modelingComputational sociolinguisticsDialectsSocial media dataSocioeconomic status

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Area of Science:

  • Computational sociolinguistics
  • Social network analysis
  • Agent-based modeling

Background:

  • Sociolinguistic studies show links between socioeconomic background and language use.
  • Quantitative analysis of how social mixing influences these linguistic correlations is limited.

Purpose of the Study:

  • To quantitatively investigate the relationship between socioeconomic status and linguistic variation.
  • To explore the impact of social mixing on language use across different socioeconomic groups.
  • To model the mechanisms driving linguistic variety adoption.

Main Methods:

  • Utilized geotagged tweets from eight UK metropolitan areas.
  • Employed transferable computational methods to map deviations from standard English.
  • Integrated high-resolution income data to derive socioeconomic indicators for users.
  • Developed an agent-based model to simulate linguistic variety adoption.

Main Results:

  • Identified a consistent pattern: increased mixing of socioeconomic classes correlates with decreased interdependence between language variation and income.
  • Observed that the frequency of departures from standard grammar becomes less tied to income in highly mixed areas.
  • The agent-based model provides a plausible mechanism for these observed linguistic patterns.

Conclusions:

  • Social mixing plays a significant role in modulating the relationship between socioeconomic status and linguistic practices.
  • Computational and modeling approaches can effectively uncover complex social dynamics in language.
  • Findings contribute to understanding language variation in diverse, urban environments.