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,thelanguageoffoodhaspredictive power. We are able to predict if states in the United States (US) are above the medianratesfortype2diabetesmellitus(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 greatestimprovementcomingfrompovertyinformation(6%),but,importantly,thelanguage 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 differentgenders. Last,weprovideanonlinevisualization tool for real-time queries and semantic analysis.

This work explores the detection of individuals’ risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity. Our approach extends a deep learning architecture with several contributions: following previous observations that language use differs by gender, it captures and uses gender information through domain adaptation; it captures recency of posts under the hypothesis that more recent posts are more representative of an individual’s current risk status; and, lastly, it demonstrates that in this scenario where activity factors are sparsely represented in the data, a bag-of-word neural network model using custom dictionaries of food and activity words performs better than other neural sequence models. Our best model, which incorporates all these contributions, achieves a risk-detection F1 of 41.9, considerably higher than the baseline rate (36.9).

We describe a strategy for the acquisition of training data necessary to build a social-media-driven early detection system for individuals at risk for (preventable) type 2 diabetes mellitus (T2DM). The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter. The questions are designed to inspire participant engagement and collect relevant data to train a public-health model applied to individuals. Prior systems designed to use social media such as Twitter to predict obesity (a risk factor for T2DM) operate on entire communities such as states, counties, or cities, based on statistics gathered by government agencies. Because there is considerable variation among individuals within these groups, training data on the individual level would be more effective, but this data is difficult to acquire. The approach proposed here aims to address this issue. Our strategy has two steps. First, we trained a random forest classifier on data gathered from (public) Twitter statuses and state-level statistics with state-of-the-art accuracy. We then converted this classifier into a 20-questions-style quiz and made it available online. In doing so, we achieved high engagement with individuals that took the quiz, while also building a training set of voluntarily supplied individual-level data for future classification.