Othering and low status framing of immigrant cuisines in US restaurant reviews and large language models
Proceedings of the International AAAI Conference on Web and Social Media(2023)
摘要
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.
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