Objective: A thematic and topic modelling analysis of sleep concerns in a social media derived, privacy-preserving, suicidality dataset. This forms the basis for an exploration of sleep as a potential computational linguistic signal in suicide prevention. Background: Suicidal ideation is a limited signal for suicide. Developments in computational linguistics and mental health datasets afford an opportunity to investigate additional signals and to consider the broader clinical ethical design implications. Methodology: A clinician-led integration of reflexive thematic analysis, with machine learning topic modelling (Bertopic), and the purposeful sampling of the University of Maryland Suicidality Dataset. Results: Sleep as a place of refuge and escape, revitalisation for exhaustion, and risk and vulnerability were generated as core themes in an initial thematic analysis of 546 posts. Bertopic analysing 21,876 sleep references in 16791 posts facilitated the production of 40 topics that were clinically interpretable, relevant, and thematically aligned to a level that exceeded original expectations. Privacy and synthetic representative data, reproducibility, validity and stochastic variability of results, and a multi-signal formulation perspective, are highlighted as key research and clinical issues.

Formulation is central to clinical practice. Formulation has a factor weighing, pattern recognition and explanatory hypothesis modelling focus. Formulation attempts to make sense of why a person presents in a certain state at a certain time and context, and how that state may be best managed to enhance mental health, safety and optimal change. Inherent to the clinical need for formulation is an appreciation of the complexities, uncertainty and limits of applying theoretical concepts and symptom, diagnostic and risk categories to human experience; or attaching meaning or weight to any particular factor in an individual?s history or mental state without considering the broader biopsychosocial and cultural context. With specific reference to suicide prevention, this paper considers the need and potential for the computer linguistic community to be both cognisant of and ethically contribute to the clinical formulation process.