Toward an integrative science of suicidality: Understanding suicide risk factors through real-world natural language.

Suicide remains a public health crisis; accurate prediction and coherent theory integration are hampered by suicidality's complexity and fragmented models.We introduce a multimethod computational framework that operationalizes 27 theory-derived suicidal constructs ("archetypes") in language.Each construct (e.g., perceived burdensomeness) was instantiated with prototype sentences (e.g., "The world would be a better place without me").Using a pretrained transformer, we embedded prototypes and computed their semantic similarity to sentences from five corpora: two real-world suicide-related (r/SuicideWatch posts, authentic suicide notes) and three controls (online discourse about mental health issues, normative stream-of-consciousness writing, and nonclinical online discourse).Corpora were profiled by archetype similarity and by Linguistic Inquiry and Word Count markers; we also benchmarked risk estimates from an instruction-tuned large language model (LLM).Suicide-related texts differed robustly from controls, with elevations across most archetypes, especially negative affect, aversive self-views, and escape motivations.r/SuicideWatch posts and suicide notes also diverged, indicating qualitative differences across stages of suicide risk.LLM ratings showed extreme anchoring and a failure to capture fine-grained gradations.Our results demonstrate that theory-guided semantic profiling can synthesize disparate suicide theories and distinguish contexts of suicidality in naturalistic text, whereas generic LLM scoring added little to this integrative approach.Across theory-derived constructs of suicide risk, we find that the highest risk of suicidality coheres around a small set of motivational cores-aversive self-evaluation, perceived burdensomeness, and a drive to escape-whose shifting balance differentiates ideation from imminent action, sharpening conceptual targets for assessment and intervention. General Scientific SummaryThis study uses computer-based language analysis to examine how people express suicidal thoughts and feelings in their own words, drawing on online posts, suicide notes, and everyday writing.By measuring how closely people's language aligns with 27 different psychological factors linked to suicide-such as feeling like a burden, wanting to escape, and painful self-focus-we find that the psychological profile of suicidality shifts depending on where a person may be in their experience of suicidal thoughts, from early distress to the point of action.