Analysis of Othering and Low Status Framing of Immigrant Cuisines in US Restaurant Reviews and Large Language Models

Core Concepts

Immigrant cuisines in US restaurant reviews and large language models are often othered and framed with low status, perpetuating harmful stereotypes.

Abstract

In this analysis, the framing of immigrant cuisines in US restaurant reviews and large language models is examined. The study focuses on the othering and low status framing of immigrant cuisines, revealing disparities in how different cuisines are portrayed. The research highlights the impact of these framing differences on social attitudes and economic outcomes for restaurants. Through linguistic analyses and regression models, the study uncovers patterns of exoticism, authenticity, luxury, cost, and hygiene framing in reviews of European, Latin American, and Asian cuisines. The findings suggest a systematic devaluation of non-European cuisines and the perpetuation of harmful stereotypes in both human and AI-generated reviews.

Directory:

1. Abstract
   • Identifying implicit attitudes toward food
   • Stereotypes about food as representational harms
   • Impact on racialized discourse and economic outcomes
2. Introduction
   • Importance of understanding implicit attitudes toward food
   • Stereotypes as a form of representational harm
   • Previous work on immigrant food attitudes
3. Data Extraction
   • "Identifying implicit attitudes toward food can mitigate social prejudice due to food’s salience as a marker of ethnic identity."
   • "Understanding the presence of representational harms in online corpora is important, given the increasing use of large language models (LLMs) for text generation."
   • "Immigrant cuisines are more likely to be othered using socially constructed frames of authenticity and exoticism."
   • "Non-European cuisines are described as cheap and dirty, even among the most expensive restaurants."
   • "Reviews generated by LLMs reproduce similar framing tendencies."
4. Quotations
   • "Immigrant food is climbing in social status, enabled by the perception and framing of immigrant food as exotic and authentic."
   • "There are status differences in cuisines due to migration patterns and resulting socioeconomic gaps."
   • "LLMs reproduce the same framing differences as Yelp reviewers."
5. Further Questions
   • How can strategies be developed to combat representational harms in online food discourse?
   • What are the implications of framing disparities in restaurant reviews on consumer behavior and economic outcomes?
   • How can AI-mediated tools be used to intervene and prevent harmful linguistic choices in reviews?

Stats

"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)."
"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."
"Reviews generated by LLMs reproduce similar framing tendencies, pointing to the downstream retention of these representational harms."

Quotes

"Immigrant food is climbing in social status, enabled by the perception and framing of immigrant food as exotic and authentic."
"There are status differences in cuisines due to migration patterns and resulting socioeconomic gaps."
"LLMs reproduce the same framing differences as Yelp reviewers."

Key Insights Distilled From

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

by Yiwe... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2307.07645.pdf

Deeper Inquiries

How can strategies be developed to combat representational harms in online food discourse?

To combat representational harms in online food discourse, several strategies can be implemented. One approach is to raise awareness among reviewers about the impact of their language choices. Providing education on cultural sensitivity and the potential consequences of biased language can help reviewers make more informed decisions when writing reviews. Additionally, platforms can implement AI tools that flag potentially harmful language and provide suggestions for more neutral or positive phrasing. These tools can serve as a real-time intervention to prevent biased reviews from being published. Furthermore, promoting diversity and inclusion in review platforms by highlighting a variety of perspectives and cuisines can help counteract stereotypes and biases.

What are the implications of framing disparities in restaurant reviews on consumer behavior and economic outcomes?

Framing disparities in restaurant reviews can have significant implications on consumer behavior and economic outcomes. When certain cuisines are consistently framed in a negative light, it can influence consumer perceptions and choices. Consumers may be less likely to visit restaurants associated with marginalized cuisines, leading to decreased foot traffic and revenue for those establishments. This can perpetuate economic inequalities and hinder the success of immigrant-owned businesses. Moreover, framing disparities can contribute to the perpetuation of stereotypes and biases, impacting the overall cultural landscape of the restaurant industry. Addressing these framing disparities is crucial for promoting diversity, equity, and inclusion in the food sector.

How can AI-mediated tools be used to intervene and prevent harmful linguistic choices in reviews?

AI-mediated tools can play a crucial role in intervening and preventing harmful linguistic choices in reviews. These tools can be designed to analyze review text in real-time and flag potentially biased or discriminatory language. By using natural language processing algorithms, AI tools can identify problematic phrases, stereotypes, or framing patterns and provide suggestions for more inclusive and respectful language. Additionally, AI tools can offer educational prompts to reviewers, highlighting the importance of cultural sensitivity and guiding them towards more neutral or positive language choices. By integrating AI-mediated tools into online review platforms, it is possible to create a more inclusive and welcoming environment for all users, while also promoting awareness and understanding of diverse cultural perspectives.