  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.
