# Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender

arXiv: https://arxiv.org/abs/2109.11061 · HTML: https://mlsanity.com/paper/2109.11061/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 99 references that are in the map.
- First posted on arXiv 22 Sep 2021.

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

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [Computational Sociolinguistics: A Survey](https://mlsanity.com/paper/1508.07544/) (2015)
- [Loyalty in Online Communities](https://mlsanity.com/paper/1703.03386/) (2017)
- [Crowdsourcing Dialect Characterization through Twitter](https://mlsanity.com/paper/1407.7094/) (2014)
- [Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization](https://mlsanity.com/paper/1404.7152/) (2014)
- [Socioeconomic Dependencies of Linguistic Patterns in Twitter: A Multivariate Analysis](https://mlsanity.com/paper/1804.01155/) (2018)

## Built on by (most cited first, 4 of 4)

- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [Queer People are People First: Deconstructing Sexual Identity Stereotypes in Large Language Models](https://mlsanity.com/paper/2307.00101/) (2023)
- [Discovering Differences in the Representation of People using Contextualized Semantic Axes](https://mlsanity.com/paper/2210.12170/) (2022)
- [Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation](https://mlsanity.com/paper/2401.04972/) (2024)

## Abstract (arXiv)

Individuals signal aspects of their identity and beliefs through linguistic choices. Studying these choices in aggregate allows us to examine large-scale attitude shifts within a population. Here, we develop computational methods to study word choice within a sociolinguistic lexical variable -- alternate words used to express the same concept -- in order to test for change in the United States towards sexuality and gender. We examine two variables: i) referents to significant others, such as the word "partner" and ii) referents to an indefinite person, both of which could optionally be marked with gender. The linguistic choices in each variable allow us to study increased rates of acceptances of gay marriage and gender equality, respectively. In longitudinal analyses across Twitter and Reddit over 87M messages, we demonstrate that attitudes are changing but that these changes are driven by specific demographics within the United States. Further, in a quasi-causal analysis, we show that passages of Marriage Equality Acts in different states are drivers of linguistic change.