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Parent information seeking and sharing: Using unsupervised machine learning to identify common parenting issues

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  • Eldeeb, Nehal
  • Ren, Cheng
  • Shapiro, Valerie B.

Abstract

Extant research shows that evidence-based parenting programs can improve parents’ knowledge, skills, and self-efficacy, leading to reduced child abuse and neglect, and promoting children’s emotional, social, and cognitive competencies. However, the impact of these programs is limited by low participation rates. Despite this, parents are actively engaging with online parenting content. Analyzing online behavior to understand parents’ information needs might illuminate topics to incorporate into parenting programs to improve parent participation. This study adopts a bottom-up Human-centered Design (HCD) approach to explore parents’ topical preferences for engaging with parenting information. Specifically, this study explores the 1) nature and prevalence of topics parents discuss online, 2) differences between mother-centric and father-centric online forums, and 3) changes in online topics since the onset of the COVID-19 pandemic. Data collected from three prominent online parenting forums from February 2019 to July 2022 were analyzed using computational methods to uncover parenting topics across audiences and time periods. Findings revealed parent-centered topics such as postpartum depression/anxiety and work-family interface, and child-centered topics such as perinatal care. Both mother- and father-centric models identified early childcare topics, as well as notable gendered topics (e.g., breast/bottle feeding for the mother-centric group vs. financial considerations for the father-centric group). The time-oriented model highlighted increased challenges in parent mental health and child education/entertainment post-COVID. Findings suggest supplementing existing parenting interventions with services that focus on parental well-being and capacity, and considering both mixed audience and group-specific interventions to address the different needs of people identifying as mothers and fathers. This user-centered approach to program design has the potential to improve parent engagement in learning positive parenting practices to reduce child maltreatment and promote child well-being.

Suggested Citation

  • Eldeeb, Nehal & Ren, Cheng & Shapiro, Valerie B., 2025. "Parent information seeking and sharing: Using unsupervised machine learning to identify common parenting issues," Children and Youth Services Review, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:cysrev:v:172:y:2025:i:c:s0190740925000933
    DOI: 10.1016/j.childyouth.2025.108210
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