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. (PsycInfo Database Record (c) 2026 APA, all rights reserved).