# NutriTransform: Estimating Nutritional Information From Online Food Posts

arXiv: https://arxiv.org/abs/2503.04755 · HTML: https://mlsanity.com/paper/2503.04755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit](https://mlsanity.com/paper/2310.12186/) (2023)
- [From Cookies to Cooks: Insights on Dietary Patterns via Analysis of Web Usage Logs](https://mlsanity.com/paper/1304.3742/) (2013)
- [Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis](https://mlsanity.com/paper/2008.08364/) (2020)

## Abstract (arXiv)

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.