The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Characterizing and predicting postpartum depression from shared facebook data
Abstract
The birth of a child is a major milestone in the life of parents. We leverage Facebook data shared voluntarily by 165 new mothers as streams of evidence for characterizing their postnatal experiences. We consider multiple measures including activity, social capital, emotion, and linguistic style in participants' Facebook data in pre- and postnatal periods. Our study includes detecting and predicting onset of post-partum depression (PPD). The work complements recent work on detecting and predicting significant postpartum changes in behavior, language, and affect from Twitter data. In contrast to prior studies, we gain access to ground truth on postpartum experiences via self-reports and a common psychometric instrument used to evaluate PPD. We develop a series of statistical models to predict, from data available before childbirth, a mother's likelihood of PPD. We corroborate our quantitative findings through interviews with mothers experiencing PPD. We find that increased social isolation and lowered availability of social capital on Facebook, are the best predictors of PPD in mothers.
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Cites
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Self-citationConnection strategies: Social capital implications of Facebook-enabled communication practices
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BackgroundLive Events, Stress, and Illness
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Cited by
Social Networking Sites, Depression, and Anxiety: A Systematic Review
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Facebook language predicts depression in medical records
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., & Schwartz, H. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802331115 · Cited by 643
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
Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics
Proferes, N., Jones, N., Gilbert, S., Fiesler, C., & Zimmer, M. (2021) · Social Media + Society · doi:10.1177/20563051211019004 · Cited by 537
“Researchers have used social media data for a wide range of purposes—from predicting postpartum depression from Facebook posts (De Choudhury et al., 2014) to trying to predict movements in the stock market based on the sentiment of Tweets (Mittal & Goel, 2010).”
Background
Metadata
- Authors
- De Choudhury, M., Counts, S., Horvitz, E. J., & Hoff, A.
- Venue
- Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
- Publisher
- ACM
- Published
- 2014-02-15
- Pages
- 626-638
- OpenAlex
- W2081249542
- Cited by
- 379
- References
- 50
- FWCI
- 316.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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- De Choudhury, M., Counts, S., Horvitz, E. J., & Hoff, A. (2014). Characterizing and predicting postpartum depression from shared facebook data. Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. https://doi.org/10.1145/2531602.2531675
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
