# Othering and low status framing of immigrant cuisines in US restaurant reviews and large language models

arXiv: https://arxiv.org/abs/2307.07645 · HTML: https://mlsanity.com/paper/2307.07645/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 80 references that are in the map.
- First posted on arXiv 14 Jul 2023.

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

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [Is Saki #delicious? The Food Perception Gap on Instagram and Its Relation to Health](https://mlsanity.com/paper/1702.06318/) (2017)
- [Social Media Image Analysis for Public Health](https://mlsanity.com/paper/1512.04476/) (2015)
- [From Cookies to Cooks: Insights on Dietary Patterns via Analysis of Web Usage Logs](https://mlsanity.com/paper/1304.3742/) (2013)
- [Fetishizing Food in Digital Age: #foodporn Around the World](https://mlsanity.com/paper/1603.00229/) (2016)
- [Biased Bytes: On the Validity of Estimating Food Consumption from Digital Traces](https://mlsanity.com/paper/2208.14401/) (2022)

## Built on by (most cited first, 2 of 2)

- [Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models](https://mlsanity.com/paper/2504.05325/) (2025)
- [What can large language models do for sustainable food?](https://mlsanity.com/paper/2503.04734/) (2025)

## Abstract (arXiv)

Identifying implicit attitudes toward food can mitigate social prejudice due to food's salience as a marker of ethnic identity. Stereotypes about food are representational harms that may contribute to racialized discourse and negatively impact economic outcomes for restaurants. Understanding the presence of representational harms in online corpora in particular is important, given the increasing use of large language models (LLMs) for text generation and their tendency to reproduce attitudes in their training data. Through careful linguistic analyses, we evaluate social theories about attitudes toward immigrant cuisine in a large-scale study of framing differences in 2.1M English language Yelp reviews. Controlling for factors such as restaurant price and neighborhood racial diversity, we find that immigrant cuisines are more likely to be othered using socially constructed frames of authenticity (e.g., "authentic," "traditional"), and that non-European cuisines (e.g., Indian, Mexican) in particular are described as more exotic compared to European ones (e.g., French). We also find that non-European cuisines are more likely to be described as cheap and dirty, even after controlling for price, and even among the most expensive restaurants. Finally, we show that reviews generated by LLMs reproduce similar framing tendencies, pointing to the downstream retention of these representational harms. Our results corroborate social theories of gastronomic stereotyping, revealing racialized evaluative processes and linguistic strategies through which they manifest.