# Socioeconomic Dependencies of Linguistic Patterns in Twitter: A Multivariate Analysis

arXiv: https://arxiv.org/abs/1804.01155 · HTML: https://mlsanity.com/paper/1804.01155/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora](https://mlsanity.com/paper/2112.14330/) (2021)
- [BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets](https://mlsanity.com/paper/2109.10234/) (2021)
- [Entropy and type-token ratio in gigaword corpora](https://mlsanity.com/paper/2411.10227/) (2024)
- [Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender](https://mlsanity.com/paper/2109.11061/) (2021)
- [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)
- [The Role of Network and Identity in the Diffusion of Hashtags](https://mlsanity.com/paper/2407.12771/) (2024)
- [The Resume Paradox: Greater Language Differences, Smaller Pay Gaps](https://mlsanity.com/paper/2307.08580/) (2023)

## Abstract (arXiv)

Our usage of language is not solely reliant on cognition but is arguably determined by myriad external factors leading to a global variability of linguistic patterns. This issue, which lies at the core of sociolinguistics and is backed by many small-scale studies on face-to-face communication, is addressed here by constructing a dataset combining the largest French Twitter corpus to date with detailed socioeconomic maps obtained from national census in France. We show how key linguistic variables measured in individual Twitter streams depend on factors like socioeconomic status, location, time, and the social network of individuals. We found that (i) people of higher socioeconomic status, active to a greater degree during the daytime, use a more standard language; (ii) the southern part of the country is more prone to use more standard language than the northern one, while locally the used variety or dialect is determined by the spatial distribution of socioeconomic status; and (iii) individuals connected in the social network are closer linguistically than disconnected ones, even after the effects of status homophily have been removed. Our results inform sociolinguistic theory and may inspire novel learning methods for the inference of socioeconomic status of people from the way they tweet.