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

2025-10-27

AI Summary

This study investigates the discursive characteristics and temporal evolution of breastfeeding-related discussions within the online health community BabyCenter. Method: Leveraging 5.43 million posts from 330,000 users, we propose a hybrid topic modeling framework integrating BERTopic and LDA: BERTopic first identifies breastfeeding-relevant discussion threads with high precision; LDA then extracts latent topics, while word-rank time-series analysis tracks diachronic shifts in key themes—including parenting anxiety, sleep, and work/childcare. Contribution/Results: We provide the first systematic evidence that parenting anxiety and sleep concerns dominate breastfeeding discourse and exhibit significant upward trends (2017–2024), reflecting escalating psychological burden. Comparative analysis between full-corpus and breastfeeding-specific models confirms distinct semantic structures and temporal dynamics for this subgroup, offering empirical grounding and a methodological paradigm for targeted digital health interventions in parenting support.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

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.

Problem

• Identifying sub-populations discussing breastfeeding in online parenting communities
• Analyzing anxiety trends in parenting forums from 2017 to 2024
• Comparing topic distributions between breastfeeding and general parenting discussions

Innovation

• Using BERTopic to locate breastfeeding discussion threads
• Applying LDA topic modeling to identify common themes
• Analyzing time series of word rank for anxiety trends