Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Selective supervision: guiding supervised learning with decision-theoretic active learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An unobtrusive behavioral model of "gross national happiness"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proactive screening for depression through metaphorical and automatic text analysis
Artificial Intelligence in Medicine
Major life changes and behavioral markers in social media: case of childbirth
Proceedings of the 2013 conference on Computer supported cooperative work
Social media as a measurement tool of depression in populations
Proceedings of the 5th Annual ACM Web Science Conference
Role of social media in tackling challenges in mental health
Proceedings of the 2nd international workshop on Socially-aware multimedia
Social networking site use by mothers of young children
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
Text classification for assisting moderators in online health communities
Journal of Biomedical Informatics
Report on the SIGIR 2013 workshop on health search and discovery
ACM SIGIR Forum
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We consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about the forthcoming influence of childbirth on the 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.