  We investigate the predictive power behind the language of food on social
media. We collect a corpus of over three million food-related posts from
Twitter and demonstrate that many latent population characteristics can be
directly predicted from this data: overweight rate, diabetes rate, political
leaning, and home geographical location of authors. For all tasks, our
language-based models significantly outperform the majority-class baselines.
Performance is further improved with more complex natural language processing,
such as topic modeling. We analyze which textual features have most predictive
power for these datasets, providing insight into the connections between the
language of food, geographic locale, and community characteristics. Lastly, we
design and implement an online system for real-time query and visualization of
the dataset. Visualization tools, such as geo-referenced heatmaps,
semantics-preserving wordclouds and temporal histograms, allow us to discover
more complex, global patterns mirrored in the language of food.
