The Anthropology of Food: How NLP can Help us Unravel the Food cultures of the World

AbstractFood carries cultural meaning beyond nutrition. It shapes identity, memory, and social norms, which makes it a central concern in anthropology. Given the diversity of food practices across cultures, analyzing them at scale while preserving their depth (“thick” descriptions) remains difficult for ethnographic methods, where Natural Language Processing (NLP) methods can help. Earlier NLP tools often captured only surface-level ”thin” descriptions. Recent methods, especially Large Language Models (LLMs), create openings to recover cultural nuance. In this position paper, we outline research questions at the intersection of food anthropology and NLP, and discuss how LLMs can enable a scalable and culturally grounded anthropology of food. We present a case study examining what LLMs represent about global eating habits, which are often shaped by colonial histories and globalization. Our findings suggest that LLMs’ internal representations recognize cultural clusters, such as shared food habits among formerly colonized regions, but fail to grasp the pragmatic and experiential aspects of food, like the worldwide spread of dishes like pizza or biryani. We conclude by highlighting some of the potential risks and gaps of using NLP for cultural analysis.