Inferring Regional and Temporal Eating Habits from Social Media Images

Understanding population level food consumption, which has considerable influence on population health, is a major challenge. For instance, obesity, which is particularly pressing in the Gulf region, is largely driven by changes in food consumption. Many other widespread diseases, such as diabetes, heart disease, and high blood pressure, are directly or indirectly affected by eating habits. Understanding food consumption generally involves surveys and self reporting which introduce certain biases, latency and substantial cost. Social media offers new possibilities to passively monitor and study eating behaviors, and track them in real-time across regions and time.

Predicting population level statistics (e.g. tracking seasonal epidemics like Flu) can be obtained through social media (e.g. shared tags and texts) which provides large scale, non-intrusive, and location-aware (regional) data in real time. Instagram is a hugely popular image sharing application, particularly in the Gulf region. Although users often annotate their social media posts with hashtags, a lot more information remains “hidden” in the actual image, requiring novel processing methods. Noting that “a picture is worth a thousand words”, we make use of this visual information through state-of-the-art deep learning models, particularly concentrated on food-related images.