# Social Perceptions of English Spelling Variation on Twitter: A Comparative Analysis of Human and LLM Responses

arXiv: https://arxiv.org/abs/2511.23041 · HTML: https://mlsanity.com/paper/2511.23041/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 37 of its 120 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Builds on (most cited first, 30 of 37)

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- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research](https://mlsanity.com/paper/2402.00159/) (2024)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
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- [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023)
- [Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination](https://mlsanity.com/paper/2406.08818/) (2024)
- [Multi-VALUE: A Framework for Cross-Dialectal English NLP](https://mlsanity.com/paper/2212.08011/) (2022)
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- [You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments](https://mlsanity.com/paper/2311.09718/) (2023)
- [The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models](https://mlsanity.com/paper/2507.16076/) (2025)
- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [Evaluation of African American Language Bias in Natural Language Generation](https://mlsanity.com/paper/2305.14291/) (2023)
- [Computational Sociolinguistics: A Survey](https://mlsanity.com/paper/1508.07544/) (2015)
- [The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models](https://mlsanity.com/paper/2502.11266/) (2025)
- [Holmes: A Benchmark to Assess the Linguistic Competence of Language Models](https://mlsanity.com/paper/2404.18923/) (2024)
- [Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks](https://mlsanity.com/paper/2410.11005/) (2024)
- [HumT DumT: Measuring and controlling human-like language in LLMs](https://mlsanity.com/paper/2502.13259/) (2025)
- ["One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviors](https://mlsanity.com/paper/2310.15398/) (2023)

## Abstract (arXiv)

Spelling variation (e.g. funnnn vs. fun) can influence the social perception of texts and their writers: we often have various associations with different forms of writing (is the text informal? does the writer seem young?). In this study, we focus on the social perception of spelling variation in online writing in English and study to what extent this perception is aligned between humans and large language models (LLMs). Building on sociolinguistic methodology, we compare LLM and human ratings on three key social attributes of spelling variation (formality, carefulness, age). We find generally strong correlations in the ratings between humans and LLMs. However, notable differences emerge when we analyze the distribution of ratings and when comparing between different types of spelling variation.