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arXiv · 2503.04755

NutriTransform: Estimating Nutritional Information From Online Food Posts

Thorsten Ruprechter·Marion Garaus·Ivo Ponocny·Denis Helic
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Abstract

Deriving nutritional information from online food posts is challenging, particularly when users do not explicitly log the macro-nutrients of a shared meal. In this work, we present an efficient and straightforward approach to approximating macro-nutrients based solely on the titles of food posts. Our method combines a public food database from the U.S. Department of Agriculture with advanced text embedding techniques. We evaluate the approach on a labeled food dataset, demonstrating its effectiveness, and apply it to over 500,000 real-world posts from Reddit's popular /r/food subreddit to uncover trends in food-sharing behavior based on the estimated macro-nutrient content. Altogether, this work lays a foundation for researchers and practitioners aiming to estimate caloric and nutritional content using only text data.

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Thorsten Ruprechter, Marion Garaus, Ivo Ponocny, Denis Helic. 2025-02-09. NutriTransform: Estimating Nutritional Information From Online Food Posts. https://arxiv.org/abs/2503.04755

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