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Thai) 1:
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ABSTRACT
We consider social media as a promising tool for public
health, focusing on the use of Twitter posts to build
predictive models about the influence of childbirth on the
forthcoming behavior and mood of new mothers. Using
Twitter posts, we quantify postpartum changes in 376
mothers along dimensions of social engagement, emotion,
social network, and linguistic style. We then construct
statistical models from a training set of observations of
these measures before and after the reported childbirth, to
forecast significant postpartum changes in mothers. The
predictive models can classify mothers who will change
significantly following childbirth with an accuracy of 71%,
using observations about their prenatal behavior, and as
accurately as 80-83% when additionally leveraging the
initial 2-3 weeks of postnatal data. The study is motivated
by the opportunity to use social media to identify mothers at
risk of postpartum depression, an underreported health
concern among large populations, and to inform the design
of low-cost, privacy-sensitive early-warning systems and
intervention programs aimed at promoting wellness
postpartum.
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