Poster #134 - Parents’ Information Seeking for Child Development Research: A Structural Topic Model of /r/ScienceBasedParenting

• In Event: Poster Session 17

Abstract

Background: Parents and other primary caregivers are key stakeholders in child development research. To effectively translate scientific discoveries into improved child outcomes, researchers need to conduct studies that have sufficient rigor and relevance to clearly guide parenting behavior. Discrepancies between parents’ information needs and the topics that researchers investigate combined with a lack of actionable research conclusions may erode public trust in child development research, as has been observed for nutritional sciences research (Garza et al., 2019). Online forums, such as Reddit, have created novel opportunities to observe parents’ information seeking behavior and could be a useful tool for increasing research relevance (Proferes et al., 2021, Westrupp et al., 2022). The purpose of this study was to identify topics that parents are actively seeking research on to inform their parenting decisions.
Methods: All original posts on the subreddit /r/ScienceBasedParenting between August 15, 2021 through August 14, 2022 were collected using an online data collection tool, PulsarTrac. Posts (N = 711, title and content combined) were analyzed using structural topic modeling. Structural topic modeling is an unsupervised machine learning approach to text analysis that generates a user-specified number of topics from a set of documents (i.e., corpus). Topics are generated based on word probabilities. Each document (in this study, each post) is assigned a probability of containing each topic such that all topic probabilities sum to 1.0. Analyses were conducted using the R programing language (R Core Team, 2022) within RStudio (Rstudio Team, 2022) with the ‘stm’ package (Roberts, Stewart, Tingley, 2019).
The final number of topics was selected based on iterative statistical and qualitative evaluation for topics ranging from K = 5 to K = 60. Statistical evaluation included semantic coherence and exclusivity. Qualitative evaluation included reviewing the top 10 words based on highest probability and the FREX metric (i.e., high frequency words that are more exclusive to each topic), and reading individual documents with the highest probability of a specific topic. After selecting the final number of topics, each topic was assigned a descriptive label. The frequency distribution of topics across the corpus was calculated by assigning each document a primary topic using the maximum topic probability.
Results: The model estimating nine topics was selected for interpretation. Figure 1 displays the distribution of topics. Figure 2 displays the highest probability words for each topic. The most frequent relevant topics were education and child development (n = 113, 18%), COVID-19 vaccines (n = 112, 18%), nutrition and food allergies (n = 91, 13%), sleep, screen time, and potty training (n = 85, 12%), and breast and/or formula feeding (n = 81, 11%).
Conclusions: Most topics reflected interest in research on pregnancy, infancy, and early childhood topics, especially practical strategies and health-related outcomes. Results were somewhat consistent with another study of general parenting subreddits (r/Daddit and r/Mommit) that found parents sought information about basic childcare situations, but also responding to challenging child behaviors, which was not an obvious topic in the current study (Westrupp et al., 2022).

Author

• Allison Doub Hepworth, Oklahoma State University - Stillwater
  Presenting Author