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.
For instance, REF analysed the behavioural changes of new mothers who are at risk of postpartum depression following childbirth.
Predicting postpartum changes in emotion and behavior via social media
CHI '13
Computer Science