  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.
