The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Predicting postpartum changes in emotion and behavior via social media
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 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.
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Cites
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BackgroundPredictors of Postpartum Depression: An Update
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BackgroundMethodYou Are What You Tweet: Analyzing Twitter for Public Health
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Cited by
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Quantifying Mental Health Signals in Twitter
Coppersmith, G., Dredze, M., & Harman, C. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3207 · Cited by 722
Methods in predictive techniques for mental health status on social media: a critical review
Chancellor, S., & De Choudhury, M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0233-7 · Cited by 557
Social media as a measurement tool of depression in populations
De Choudhury, M., Counts, S., & Horvitz, E. (2013) · Proceedings of the 5th Annual ACM Web Science Conference · doi:10.1145/2464464.2464480 · Cited by 497
Self-citationInstagram photos reveal predictive markers of depression
Reece, A. G., & Danforth, C. M. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0110-z · Cited by 519
Metadata
- Authors
- De Choudhury, M., Counts, S., & Horvitz, E.
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2013-04-27
- Pages
- 3267-3276
- OpenAlex
- W2162051395
- Cited by
- 462
- References
- 32
- FWCI
- 61.88
- Type
- conference paper
- Language
- en
- Open access
- closed
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