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Detecting sub-populations in online health communities: A mixed-methods exploration of breastfeeding messages in BabyCenter Birth Clubs

Calla Beauregard, Parisa Suchdev, Ashley Fehr, Isabelle T. Smith, Tabia Tanzin Prama, Julia Witte Zimmerman, Carter Ward, Juniper Lovato, Christopher M. Danforth, and Peter Sheridan Dodds

Times cited: 0

Abstract:

Parental stress is a nationwide health crisis according to the U.S. Surgeon General's 2024 advisory. To allay stress, expecting parents seek advice and share experiences in a variety of venues, from in-person birth education classes and parenting groups to virtual communities, for example, BabyCenter, a moderated online forum community with over 4 million members in the United States alone. In this study, we aim to understand how parents talk about pregnancy, birth, and parenting by analyzing 5.43M posts and comments from the April 2017–January 2024 cohort of 331,843 BabyCenter "birth club" users (that is, users who participate in due date forums or "birth clubs" based on their babies' due dates). Using BERTopic to locate breastfeeding threads and LDA to summarize themes, we compare documents in breastfeeding threads to all other birth-club content. Analyzing time series of word rank, we find that posts and comments containing anxiety-related terms increased steadily from April 2017 to January 2024. We used an ensemble of topic models to identify dominant breastfeeding topics within birth clubs, and then explored trends among all user content versus those who posted in threads related to breastfeeding topics. We conducted Latent Dirichlet Allocation (LDA) topic modeling to identify the most common topics in the full population, as well as within the subset breastfeeding population. We find that the topic of sleep dominates in content generated by the breastfeeding population, as well anxiety-related and work/daycare topics that are not predominant in the full BabyCenter birth club dataset.
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BibTeX:

@article{beauregard2025a,
  author =	 {Beauregard, Calla and Suchdev, Parisa and Fehr,
                  Ashley and Smith, Isabelle T. and Prama, Tabia
                  Tanzin and Zimmerman, Julia Witte and Ward, Carter
                  and Lovato, Juniper and Danforth, Christopher M. and
                  Dodds, Peter Sheridan},
  title =	 {Detecting sub-populations in online health
                  communities: {A} mixed-methods exploration of
                  breastfeeding messages in {B}aby{C}enter birth clubs},
  journal =	 {arXiv preprint arXiv:2510.23692},
  year =	 {2025},
  key =		 {},
  url =		 {https://arxiv.org/abs/2510.23692},
}
 
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