JMIR Publications

ABSTRACT

Background:

Health technology innovation is increasingly supported by a bottom-up approach to priority setting, aiming to better reflect the concerns of its intended beneficiar-ies. Online forums provide parents with an outlet to share concerns, advice, and information related to parenting and the health and wellbeing of their children. They provide a rich source of data on parenting concerns and priorities that could inform future child health research and innovation.

Objective:

To identify common concerns expressed on two major online forums and cluster these to identify potential family health concern topics as indicative priority areas for future research and innovation.

Methods:

We text-mined the r/Parenting subreddit (69846 posts) and the parenting section of Mumsnet (99848 posts) to create a large corpus of posts. A generative statistical model (latent Dirichlet allocation – LDA) was used to identify the most discussed topics in the corpus and content analysis applied to identify the parenting concerns found in a subset of posts.

Results:

A model with 25 topics produced the highest coherence and a wide range of mean-ingful parenting concern topics. The most frequently expressed parenting concerns related to their child’s sleep, self-care, eating (and food), behavior, child-care con-text, and the parental context including parental conflict. Topics directly associated with infants, such as potty training and bottle feeding, were more common on Mumsnet, while parental context and screen time were more common on r/Parenting.

Conclusions:

LDA topic modelling can be applied to gain a rapid, yet meaningful overview of parent concerns expressed on a large and diverse set of social media posts and used to complement traditional insight gathering methods. Parents framed their concerns in terms of children’s everyday health concerns, generating topics that overlap significantly with established family health concern topics. We provide evi-dence of the range of family health concerns found at these sources and hope this can be used to generate material for use alongside traditional insight gathering methods.