Accepted for/Published in: JMIR Mental Health
Date Submitted: Feb 8, 2024
Date Accepted: Mar 29, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Suicidality in a Social Media-based Taxonomy of Mental Health Disorders
ABSTRACT
Background:
To understand natural language use during public online discussions around topics related to suicidality.
Objective:
We used large language model-based sentence embedding to extract the latent linguistic dimensions of user postings derived from several mental health related subreddit channels, with a focus on suicidality. We then apply dimensionality reduction to these sentence embeddings, allowing them to be summarized and visualized in a lower dimensional Euclidean space for further downstream analyses.
Methods:
We analyzed 2.9 million posts extracted from 30 subreddit channels, including suicide watch, between October 1st, 2022, and December 31st, 2022, and the same period in 2010.
Results:
Our results showed that, in line with existing theories of suicide, posters in the suicidality community ("Suicide Watch") predominantly wrote about feelings of disconnection, burdensomeness, hopeless, desperation, resignation, and trauma. Further, we identified distinct latent linguistic dimensions (well-being, seeking support, and severity of distress) among all mental health subreddits, and the resulting subreddit clusters were in line with a statistically-driven diagnostic classification system - namely the Hierarchical Taxonomy of Psychopathology (HiTOP) - by mapping onto purposed superspectra.
Conclusions:
Overall, our findings provide data-driven support for several language-based theories of suicide as well as dimensional classification systems for mental health disorders. Ultimately, this novel combination of natural language processing techniques can assist researchers in gaining deeper insights about emotions and experiences shared online, and may aid in the validation and refutation of different mental health theories.
Citation
