Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers

Abstract

We manually annotated 9734 tweets that were posted by users who reported their pregnancy on Twitter, and used them to train, evaluate, and deploy deep neural network classifiers (F₁-score=0.93) to detect tweets that report having a child with attention-deficit/hyperactivity disorder (678 users), autism spectrum disorders (1744 users), delayed speech (902 users), or asthma (1255 users), demonstrating the potential of Twitter as a complementary resource for assessing associations between pregnancy exposures and childhood health outcomes on a large scale.

Keywords: Twitter; asthma; data mining; developmental disabilities; epidemiology; machine learning; natural language processing; pregnancy; social media.