NutriTransform: Estimating Nutritional Information From Online Food Posts
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915184756391936 |
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| author | Ruprechter, Thorsten Garaus, Marion Ponocny, Ivo Helic, Denis |
| author_facet | Ruprechter, Thorsten Garaus, Marion Ponocny, Ivo Helic, Denis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NutriTransform: Estimating Nutritional Information From Online Food Posts Ruprechter, Thorsten Garaus, Marion Ponocny, Ivo Helic, Denis Computers and Society Computation and Language 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. |
| title | NutriTransform: Estimating Nutritional Information From Online Food Posts |
| topic | Computers and Society Computation and Language |
| url | https://arxiv.org/abs/2503.04755 |