Reddit's Appetite: Predicting User Engagement with Nutritional Content

2025-02-11

AI Summary

This study investigates how the nutritional density of food-related content on social media influences user engagement, focusing on nearly 600,000 food-related posts from Reddit. Leveraging the USDA FoodData Central database, we automatically annotated caloric and macronutrient content and trained an XGBoost model to predict engagement metrics—including comment count—while assessing feature importance and conducting ablation studies to isolate the impact of nutritional features. Results demonstrate, for the first time, that nutritional density—particularly caloric density—exerts a statistically significant positive effect on user interaction: incorporating nutritional features improves model accuracy by 4%, with caloric density emerging as the most predictive positive factor. This work bridges a critical empirical gap in understanding how nutrition-informed textual features drive social dissemination behavior and provides a data-driven, interpretable foundation for digital interventions promoting healthy eating.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Machine Learning: Feature Construction/Reformulation
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Social Networks and Social Media: Computational social science

Abstract

The increased popularity of food communities on social media shapes the way people engage with food-related content. Due to the extensive consequences of such content on users' eating behavior, researchers have started studying the factors that drive user engagement with food in online platforms. However, while most studies focus on visual aspects of food content in social media, there exist only initial studies exploring the impact of nutritional content on user engagement. In this paper, we set out to close this gap and analyze food-related posts on Reddit, focusing on the association between the nutritional density of a meal and engagement levels, particularly the number of comments. Hence, we collect and empirically analyze almost 600,000 food-related posts and uncover differences in nutritional content between engaging and non-engaging posts. Moreover, we train a series of XGBoost models, and evaluate the importance of nutritional density while predicting whether users will comment on a post or whether a post will substantially resonate with the community. We find that nutritional features improve the baseline model's accuracy by 4%, with a positive contribution of calorie density towards prediction of engagement, suggesting that higher nutritional content is associated with higher user engagement in food-related posts. Our results provide valuable insights for the design of more engaging online initiatives aimed at, for example, encouraging healthy eating habits.

Problem

• Predicting user engagement with food content on Reddit
• Analyzing nutritional density impact on engagement
• Improving model accuracy using nutritional features

Innovation

• Analyzes nutritional content impact
• Uses XGBoost models for prediction
• Links calorie density to engagement