Othering and low status framing of immigrant cuisines in US restaurant reviews and large language models
Yiwei Luo, Kristina Gligori\'c, Dan Jurafsky

TL;DR
This study analyzes 2.1 million Yelp reviews to uncover racialized stereotypes and othering of immigrant cuisines, revealing biases in human and AI-generated text that may reinforce social prejudices.
Contribution
It provides large-scale linguistic evidence of racialized framing of immigrant cuisines in online reviews and shows that large language models reproduce these biases.
Findings
Immigrant cuisines are more often othered with authenticity frames.
Non-European cuisines are described as more exotic, cheap, and dirty.
LLMs reproduce similar framing biases found in human reviews.
Abstract
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…
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Taxonomy
TopicsCulinary Culture and Tourism
