The impact of depression forums on illness narratives: a comprehensive NLP analysis of socialization in e-mental health communities

While depression is globally on the rise, the mental health sector struggles with handling the increased number of cases, especially since the pandemic. These circumstances have resulted in an increased interest in the e-mental health sector. The dataset is constituted of 67 857 posts from the most popular English-language online health forums between 15 February 2016 and 15 February 2019. The posts were first automatically labelled (biomedical vs. psy framing) via deep learning; second, the time series of framing types of recurring forum users were analysed; third, the clusters of biomedical and psy patterns were analysed; fourth, the discursive characteristics of each cluster were analysed with the help of topic modelling. Five ideal-typical patterns of forum socialization are described: the first and the second clusters express the developing of a ‘recovery helper’ role, either by opposing expert discourses or by identifying with the psy discourses; the third cluster expresses the acquiring of a substantively diffuse, uncertain role; the fourth and fifth clusters refer to a trajectory leading to the incorporating of a biomedically framed patient role, or a therapeutic psy subjectivity. Elements of data collection that potentially undermine representativeness: online forum users, open and public forums, keyword search. The trajectories identified in our study represent various phases of a general forum socialization process: newcomers (cluster 3); settled patient role (cluster 4) or psy subjectivity (cluster 5); recovery helpers (cluster 1 and 2).