ArticlePloS one2020
Pregnancy and health in the age of the Internet: A content analysis of online "birth club" forums.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Remotely Delivered Interventions to Support Women With Symptoms of Anxiety in Pregnancy: Mixed Methods Systematic Review and Meta-analysis.Journal of medical Internet research · 2022Pooled it
- Informational needs in vulvar cancer and precursor conditions: a social listening study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Evaluating online information on reduced fetal movements: a mixed-methods study.Irish journal of medical science · 2026Article
- Impacts of Sexual and Reproductive Health and Rights Misinformation in Digital Spaces on Human Rights Protection and Promotion: Scoping Review.JMIR infodemiology · 2025Article
- Quantifying Maternal Health Using Digital Phenotyping: Protocol for a Longitudinal Observational Study.JMIR research protocols · 2025Article
- User Satisfaction With Pregnancy Management Apps in Mainland China: User-Generated Content Analysis and Text Mining Study.Journal of medical Internet research · 2025Article
- Examining the inaccurate reproductive health messaging of crisis pregnancy centers: A comparative qualitative study.PloS one · 2025Article
- Prenatal alcohol exposure before pregnancy awareness: a thematic analysis of online forum comments and misinformation.Frontiers in public health · 2025Article
- Reenvisioning Title X to Meet Early Pregnancy Needs.American journal of public health · 2024Article
- A Descriptive Analysis of a Popular Pregnancy Forum: Comments on the Developmental Consequences of Cannabis Use on Offspring.Journal of studies on alcohol and drugs · 2024Review
- Digital Health Needs and Preferences During Pregnancy and the Postpartum Period: Mixed Methods Study.JMIR formative research · 2024Article
- Narratives of pregnancy across 19 Countries: Analysis of a 1.5-billion-word news media database.PloS one · 2024Article
- Personalization and localization as key expectations of digital health intervention in women pre- to post-pregnancy.NPJ digital medicine · 2023Article
- Content Quality of YouTube Videos About Pain Management After Cesarean Birth: Content Analysis.JMIR infodemiology · 2023Article
- Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach.Journal of medical Internet research · 2023Article
- Experiences of giving birth during the COVID-19 pandemic: a qualitative analysis of social media comments through the lens of birth integrity.BMC pregnancy and childbirth · 2023Article
- Public Engagement in Digital Recommendations for Promoting Healthy Parental Behaviours from Preconception through the First 1000 Days.International journal of environmental research and public health · 2023Article
- Infodemic management challenges and evidence-based midwifery.European journal of midwifery · 2023Article
- Researching Alcohol Consumption During Pregnancy. Opportunities and Challenges With Two Methods of Data Acquisition.Qualitative health research · 2022Article
- Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study.Journal of medical Internet research · 2022Observational
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundAlthough studies report that more than 90% of pregnant women utilize digital sources to supplement their maternal healthcare, little is known about the kinds of information that women seek from their peers during pregnancy. To date, most research has used self-report measures to elucidate how and why women to turn to digital sources during pregnancy. However, given that these measures may differ from actual utilization of online health information, it is important to analyze the online content pregnant women generate.
objectiveTo apply machine learning methods to analyze online pregnancy forums, to better understand how women seek information from a community of online peers during pregnancy.
methodsData from seven WhatToExpect.com "birth club" forums (September 2018; January-June 2018) were scraped. Forum posts were collected for a one-year period, which included three trimesters and three months postpartum. Only initial posts from each thread were analyzed (n = 262,238). Automatic natural language processing (NLP) methods captured 50 discussed topics, which were annotated by two independent coders and grouped categorically.
resultsThe largest topic categories were maternal health (45%), baby-related topics (29%), and people/relationships (10%). While pain was a popular topic all throughout pregnancy, individual topics that were dominant by trimester included miscarriage (first trimester), labor (third trimester), and baby sleeping routine (postpartum period).
conclusionMore than just emotional or peer support, pregnant women turn to online forums to discuss their health. Dominant topics, such as labor and miscarriage, suggest unmet informational needs in these domains. With misinformation becoming a growing public health concern, more attention must be directed toward peer-exchange outlets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.