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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting postpartum changes in emotion and behavior via social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Characterizing and predicting postpartum depression from shared facebook data
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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We explore the harnessing of social media as a window on changes around major life events in individuals and larger populations. We specifically examine patterns of activity, emotional, and linguistic correlates for childbirth and postnatal course. After identifying childbirth events on Twitter, we analyze daily posting patterns and language usage before and after birth by new mothers, and make inferences about the status and dynamics of changes in emotions expressed following childbirth. We find that childbirth is associated with some changes for most new mothers, but approximately 15% of new mothers show significant changes in their online activity and emotional expression postpartum. We observe that these mothers can be distinguished by linguistic changes captured by shifts in a relatively small number of words in their social media posts. We introduce a greedy differencing procedure to identify the type of language that characterizes significant changes in these mothers during postpartum. We conclude with a discussion about how such characterizations might be applied to recognizing and understanding health and well-being in women following childbirth.