Analysis of parenting informational needs for mothers with infants and toddlers using text-mining

Sunhwa Park, Kyungja June and Jungok Yu

Children and Youth Services Review, 2023, vol. 145, issue C

Abstract: Due to its accessibility and anonymity, the Internet is a common way for mothers to find information about parenting concerns. The purpose of this study was to explore the parenting questions posted by mothers on parenting online communities to identify the informational needs of parents with young children. We analyzed 31,565 parenting–related questions posted in October 2019. Text mining was performed using Latent Dirichlet Allocation (LDA) to identify the most common topics in the dataset. Topics were sorted according to infant age group and a keyword network analysis was performed to identify parenting difficulties. In the LDA, 15 topics were found to be the best-fitting model. From these, we identified parenting situations that lead to many questions, the majority of which were focused on basic childcare related to feeding, baby food, sleeping, medical problems, development, and learning. The distribution of the 15 topics varies according to age group. Keyword network analysis showed that mothers had difficulty putting their babies to sleep and stopping them from crying. This study provides useful information to support parenting and makes suggestions for how parenting programs can meet mothers’ needs.

Keywords: Parenting; Mothers; Infant; Toddler; Text-mining (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:cysrev:v:145:y:2023:i:c:s0190740922004042

DOI: 10.1016/j.childyouth.2022.106768

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