You Tweet What You Eat: Studying Food Consumption Through Twitter

TL;DR

This study explores how Twitter data can be used to analyze dietary habits and predict obesity and diabetes rates across US regions, revealing correlations with demographics and social networks.

Contribution

It introduces a novel method of using Twitter food mentions to estimate regional health metrics and social influences on eating habits.

Findings

Caloric content of foods mentioned correlates with state obesity rates (r=0.77).

Twitter food data improves obesity and diabetes prediction models.

Higher education levels are linked to lower-calorie food mentions.

Abstract

Food is an integral part of our lives, cultures, and well-being, and is of major interest to public health. The collection of daily nutritional data involves keeping detailed diaries or periodic surveys and is limited in scope and reach. Alternatively, social media is infamous for allowing its users to update the world on the minutiae of their daily lives, including their eating habits. In this work we examine the potential of Twitter to provide insight into US-wide dietary choices by linking the tweeted dining experiences of 210K users to their interests, demographics, and social networks. We validate our approach by relating the caloric values of the foods mentioned in the tweets to the state-wide obesity rates, achieving a Pearson correlation of 0.77 across the 50 US states and the District of Columbia. We then build a model to predict county-wide obesity and diabetes statistics based on a combination of demographic variables and food names mentioned on Twitter. Our results show significant improvement over previous CHI research (Culotta'14). We further link this data to societal and economic factors, such as education and income, illustrating that, for example, areas with higher education levels tweet about food that is significantly less caloric. Finally, we address the somewhat controversial issue of the social nature of obesity (first raised by Christakis & Fowler in 2007) by inducing two social networks using mentions and reciprocal following relationships.