In this work we investigate the signal contained in the language of food on social media. We experiment with a dataset of 24 million food-related tweets, and make several observations. First, the language of food has predictive power. We are able to predict if states in the United States (US) are above the median rates for type 2 diabetes mellitus (T2DM), income, poverty, and education - outperforming previous work by 4-18%. Second, we investigate the effect of socioeconomic factors (income, poverty, and education) on predicting state-level T2DM rates. Socioeconomic factors do improve T2DM prediction, with the greatest improvement coming from poverty information (6%), but, importantly, the language of food adds distinct information that is not captured by socioeconomics. Third, we analyze how the language of food has changed over a five-year period (2013 - 2017), which is indicative of the shift in eating habits in the US during that period. We find several food trends, and that the language of food is used differently by different groups such as different genders. Last, we provide an online visualization tool for real-time queries and semantic analysis.