Programmatic Theme: Data Science

Abstract: Despite the prevalence of adverse pregnancy outcomes such as miscarriage, stillbirth, birth defects, and preterm birth, their causes are largely unknown. We seek to advance the use of social media for observational studies of pregnancy outcomes by developing a natural language processing pipeline for automatically identifying users from which to select comparator groups on Twitter. We annotated 2361 tweets by users who have announced their pregnancy on Twitter, which were used to train and evaluate supervised machine learning algorithms as a basis for automatically detecting women who have reported that their pregnancy had reached term and their baby was born at a normal weight. Upon further processing the tweet-level predictions of a majority voting-based ensemble classifier, the pipeline achieved a user-level F₁-score of 0.933, with a precision of 0.947 and a recall of 0.920. Our pipeline will be deployed to identify large comparator groups for studying pregnancy outcomes on Twitter.

Learning Objective: After attending this presentation, the learner should be better able to understand how comparator groups can be identified for observational studies of pregnancy outcomes on Twitter.

Authors:

Ari Klein (Presenter)
University of Pennsylvania

Abeselom Gebreyesus, University of Maryland, Baltimore County
Graciela Gonzalez-Hernandez, University of Pennsylvania