NutriTransform: Estimating Nutritional Information From Online Food Posts
This work addresses the problem of estimating nutritional content from text data for researchers and practitioners, particularly those interested in analyzing online food-sharing behavior.
The authors tackled the problem of estimating nutritional information from online food posts, achieving this by applying their method to over 500,000 real-world posts. Their approach demonstrated effectiveness in uncovering trends in food-sharing behavior based on estimated macro-nutrient content.
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.