Maternal mental health monitoring in an online community: a natural language processing approach
Zhen Zhu
Behaviour and Information Technology, 2025, vol. 44, issue 10, 2379-2388
Abstract:
Digital maternity support communities are increasingly popular. The communities are often based on discussion forums called ‘birth clubs’, to which users are assigned according to their estimated due months. Distinguishing between support-seeking and non-support-seeking posts submitted to these ‘birth clubs’ is a crucial first step for monitoring maternal mental health. This study utilised natural language processing (NLP) techniques on 52,558 posts collected from one of the largest online maternity communities in China, employing machine learning algorithms trained for post classification with a randomly selected and manually labelled subset of 3000 posts. The results validated the properties of information similarity and time sensitivity within the post data, and demonstrated the feasibility of employing simple algorithms and small training sets for effective maternal mental health monitoring.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/0144929X.2024.2333927 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tbitxx:v:44:y:2025:i:10:p:2379-2388
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tbit20
DOI: 10.1080/0144929X.2024.2333927
Access Statistics for this article
Behaviour and Information Technology is currently edited by Dr Panos P Markopoulos
More articles in Behaviour and Information Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().