Ethical thematic and topic modelling analysis of sleep concerns in a social media derived suicidality dataset (2024)

Abstract: 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.

## introduction this paper reports on the thematic and topic modelling analysis of sleep concerns in a social mediaderived, privacy-preserving, suicidality dataset. key objectives are an exploration of 1) the role of sleep as a potential linguistic signal in suicide prevention formulation, and 2) the ethical and design opportunities and challenges, artificial intelligence (ai) and sensitive mental health datasets may afford to the global mental health community @xcite @xcite @xcite . this work arises from an academic program, centered on clinical best practice, leadership, and change and is the third in a series of papers focused on ai, ethics and suicide prevention @xcite . there is increasing interest in the application of computational linguistics in suicide prevention. suicide prevention threat detection, and guardian angel-type technology, are already deployed in social media. this raises significant ethical design, clinical effectiveness, and governance issues @xcite @xcite @xcite . suicidal ideation is a relatively weak signal for suicide, and there is a need to research additional signals @xcite @xcite . sleep disturbance is associated with increased suicidal behaviour to the degree it may represent a modifiable risk factor and signal to inform suicide risk management formulation and intervention planning @xcite @xcite @xcite @xcite . computational linguistics is a branch of ai, associated with natural language processing. it has come to increasing clinical importance because of the rapid advancements and growth in transformers and generative ai @xcite . closely entwined with the use of ai in mental health, is the ethical utilisation and governance of sensitive data, including data mining and the curation of datasets. the data utilisation approach in this research combines the qualitative braun and clarke approach to thematic analysis, with quantitative machine learning topic modelling, to study a large social media suicidality dataset @xcite @xcite @xcite . there is emerging interest in the potential research benefits of combining qualitative reflexive thematic analysis with computational methods. this includes how topic modelling could assist with more rapid familiarization and coding of large social media-based data sets, and contribute to the qualitative, nuanced, contextual, interpretative, and reflexive theme creation process @xcite . suicide is a complex devastating event where there can be a struggle to make sense, find meaning, and understand the related computational basis of the mind, lived experience, intent, and decisionmaking. computational neuroscience and psychiatry seek to bring understanding to complex human behaviours to optimize care and prevention @xcite . computational linguistics can play a role in data collection, formulation, and the creation of digital psychotherapeutic interventions. either individually or combined with other neurophysiological, behavioural, and imaging techniques, computational linguistics may provide modelling insights into the neural basis of language @xcite . this may contribute to the development, analysis, and detection of language biomarkers or signals of various states of cognitive and emotional processing, mental disorder, and behavioural risk. suicidal ideation is an important risk signal for potential psychological distress and completed suicide that requires timely assessment and intervention. however, although important, it is also a weakly predictive, frequently late, or nonpresenting and unreliable signal @xcite @xcite . although there is only a limited correlation between suicidal ideation and suicide, those who have expressed suicidal ideation may be a significant target population to look for other signals operating with different contextual factors, frequency, prevalence, and time scales that may assist with the timely formulation of risk management and suicide prevention @xcite . ## discussion this was exploratory research, with a focus on reporting and contextualising the analysis process and results, to a clinical /non-data scientist audience, to particularly highlight conceptual, ethical, and design issues for future research, and development. the following section aims to explore further a number of these issues. ## conclusion this paper reported on the thematic and computational linguistic topic modelling analysis of sleep concerns in the university of maryland suicidality dataset. this was multidisciplinary, exploratory, foundational work, that had the broader aim of highlighting some of the conceptual, ethical, and design opportunities and challenges artificial in-telligence and mental health datasets may afford. the reflexive thematic analysis produced three core themes; sleep as a place of refuge and escape, risk and vulnerability, and revitalization for exhaustion. bertopic was utilized to produce 40 topics with representative key terms and documents. the combined thematic analysis, and topic modelling process, resulted in clinically interpretable, relevant, and aligned results that exceeded initial expectations. this is the third in a series of papers focused on ai and suicide prevention. central series themes have been the complexities and contentions of suicide prediction, the related central role of formulation in clinical practice, and how the computational linguistic detection, development, and integration of relevant signals may contribute to enhancing the formulation and intervention planning process. sleep is a potentially useful linguistic signal in ai-based suicide risk formulation and intervention planning. establishing sleep themes, topics, and key terms represents an initial exploratory development stage. a deployed ai-enhanced social mediabased system that could detect, and utilize a linguistic sleep signal would need significant ethical codesign and governance. research and development would require an iterative multistage, multimodal, multisignal contextually and culturally aware integrated formulation approach. sleep may be considered as both a signal and an intervention and more conceptually as a preferable escape for a traumatised, overwhelmed, and exhausted consciousness. sleep may help with cognitive and emotional processing, decreasing impulsivity, and increasing the capability to see a positive path through. an advantage of focusing on sleep as a key signal is that it can be both a transdiagnostic indicator of illness and vulnerability, and a transdiagnostic positive intervention, maximising the opportunities for benefit and optimisation of scarce resource utilisation. suicide is a complex, multifactorial low-base rate event, where there are significant risks and limitations in prediction and particularly attaching specific predictive or intervention power to any standalone factor. sleep and sleep disturbance may have a significant role as a sensitive indicator of human distress and arousal and mental health vulnerability and as a signal that further assessment and intervention are required. sleep and sleep disturbance signal data may be coded, labelled, weighed, and used in the formulation both as an indicator of transdiagnostic emotional distress and arousal, and to contribute towards specific mental disorder and sleep disorder interventions. it may also be more acceptable or in keeping with cultural, group, or personal experiential norms to talk about sleep disturbance and related exhaustion than to report depression or suicidal ideation. data relating to suicide is highly sensitive, and privacy-preserving datasets may afford clinical safety, medicolegal, and ethical benefits. social media data mining affords an opportunity, to improve the computational understanding of human behavior and provide insights into user mental models. this may enhance psychological formulation, targeted needs segmentation, and personalized timely, user experience, engagement, recommendation, and intervention planning. this is particularly important in suicide prevention. in terms of practical clinical contributions, arising from this research, the findings have contributed to the psychiatrist first author's design of digital intake forms for a specialist sleep clinic. the future aim would be to develop a multimodal ai-enhanced assessment and formulation system, that was also capable of assisting with the therapeutic revision of memory and narrative associated with traumatic nightmares. nightmares are associated with suicidality and were a significant finding of both the thematic analysis and topic modelling aspects of this research. in terms of methodological contributions, the integration of reflexive thematic analysis and machine learning topic modelling could benefit other researchers working with large datasets that require scale, speed, and interpretive nuance in analysis. the integrated approach may contribute to the explicability or understanding of the algorithmic process and thematic and topic results. as the ai field moves from large language models to merged, multimodal models (triple m's), computational linguistics should retain a central role in sensing, shaping, augmenting, and amplifying the human psyche toward creative effective action and outputs. there is a need to research how large language models and generative ai could be used in fine-tuning, and clinically relevant analysis and interventions, in a way that addresses the sensitive nature of potential mental health and riskrelated data and the associated medicolegal, ethical, clinical effectiveness, and safety issues. the stated vision for the clpsych community is to improve interdisciplinary knowledge exchange, foster collaboration, and increase the visibility of mental health as a problem domain in natural lan-guage processing. similarly important is improving the visibility and accessibility of computational linguistics as an opportunity domain in clinical practice. the rapid rise in pre-trained transformers and generative ai has increased the need for clinician knowledge, education, and engagement in computational linguistics. also highlighted by these developments is a broader need for multistakeholder research, co-design and representative governance to enhance the capacity and propensity for benefit optimization and harm minimisation. key related areas for future research and development highlighted by this paper include building a shared understanding and approach to, risk-benefit analysis, power and consent, formulation, stochastics, and sensitive and representative data. by focusing on one signal (sleep), one dataset, and one technique (topic modelling) a core aim of this research is a greater shared conceptual understanding of the opportunities and challenges presented by computational linguistics to the global mental health community. global mental health may be particularly suited and vulnerable to developments in computational linguistics due to the central role of language in the psyche and culture and the need for contextual weighting in making sense of and shaping complex human experiences. the computational linguistic and global mental health communities need to engage with these challenges and opportunities with a high degree of shared ethical, conceptual, contextual, cultural, and power dynamic awareness. this paper has aimed to enhance that awareness and contribute to the developmental capacity of the clpsych community to pursue its mission to reduce emotional suffering and suicide. a key issue in social media research is that verbatim quotes can not infrequently be traced back to the source, and with cross-referencing of other aspects of content or style, the online poster is potentially identified. these composite synthetic quotes, aim to illustrate the type of content that was the basis of the reflexive thematic analysis generated themes, without infringing the ethical undertaking not to use verbatim quotes.