Towards using social media to identify individuals at risk for preventable chronic illness
This addresses the challenge of individual-level data acquisition for public health models targeting preventable chronic illnesses like type 2 diabetes, though it is incremental as it builds on prior community-based methods.
The paper tackles the problem of acquiring individual-level training data for predicting type 2 diabetes risk from social media, which is difficult due to reliance on community-level statistics, by developing a game-like quiz from Twitter data that achieved high engagement and built a dataset for future classification.
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.