Paper Info
Title
Towards using social media to identify individuals at risk for preventable chronic illness.
Abstract
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.
Year
Venue
Keywords
2016
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
machine learning,obesity detection,social media
DocType
Volume
Citations 
Conference
abs/1603.03784
0
PageRank 
References 
Authors
0.34
14
5
Name
Order
Citations
PageRank
Dane Bell183.64
Daniel Fried2837.69
Luwen Huangfu3346.23
Mihai Surdeanu42582174.69
Stephen G. Kobourov51440122.20