Towards scaling Twitter for digital epidemiology of birth defects.

Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes-the leading cause of infant mortality-could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms-feature-engineered and deep learning-based classifiers-that automatically distinguish tweets referring to the user's pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F

Informations de copyright

© The Author(s) 2019.

Déclaration de conflit d'intérêts

Competing interestsThe authors declare no competing interests.