# Impoverished Language Technology: The Lack of (Social) Class in NLP

arXiv: https://arxiv.org/abs/2403.03874 · HTML: https://mlsanity.com/paper/2403.03874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI](https://mlsanity.com/paper/2109.09483/) (2021)
- [Computational Sociolinguistics: A Survey](https://mlsanity.com/paper/1508.07544/) (2015)
- [Socially Aware Bias Measurements for Hindi Language Representations](https://mlsanity.com/paper/2110.07871/) (2021)
- [The Danish Gigaword Project](https://mlsanity.com/paper/2005.03521/) (2020)
- [The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions](https://mlsanity.com/paper/1808.09600/) (2018)
- [Residualized Factor Adaptation for Community Social Media Prediction Tasks](https://mlsanity.com/paper/1808.09479/) (2018)
- [You Write Like You Eat: Stylistic variation as a predictor of social stratification](https://mlsanity.com/paper/1907.07265/) (2019)

## Built on by (most cited first, 1 of 1)

- [Variation is the Norm: Embracing Sociolinguistics in NLP](https://mlsanity.com/paper/2603.24222/) (2026)

## Abstract (arXiv)

Since Labov's (1964) foundational work on the social stratification of language, linguistics has dedicated concerted efforts towards understanding the relationships between socio-demographic factors and language production and perception. Despite the large body of evidence identifying significant relationships between socio-demographic factors and language production, relatively few of these factors have been investigated in the context of NLP technology. While age and gender are well covered, Labov's initial target, socio-economic class, is largely absent. We survey the existing Natural Language Processing (NLP) literature and find that only 20 papers even mention socio-economic status. However, the majority of those papers do not engage with class beyond collecting information of annotator-demographics. Given this research lacuna, we provide a definition of class that can be operationalised by NLP researchers, and argue for including socio-economic class in future language technologies.