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.
@article{arxiv.2503.04755,
title = {NutriTransform: Estimating Nutritional Information From Online Food Posts},
author = {Thorsten Ruprechter and Marion Garaus and Ivo Ponocny and Denis Helic},
journal= {arXiv preprint arXiv:2503.04755},
year = {2025}
}