Abstract
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.
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