LANGUAGE OF STRESS: A CORPUS-BASED STUDY TO DETECT EARLYSIGNS OF SUICIDE THROUGH LEXICAL CHOICE
Keywords:
corpus linguistics, suicide prevention, lexical analysis, language of stress, computational stylistics, mental health discourse, psycholinguistics, natural language processing, Keynes, LIWCAbstract
Suicide represents one of the most devastating and preventable causes of premature death globally, yet its
early detection remains stubbornly elusive. This study advances the hypothesis that language specifically the
spontaneous lexical choices individuals make in everyday written and digital discourse constitutes one of the
most sensitive and accessible markers of suicidal ideation. We employ corpus-based methodologies to
conduct a systematic, quantitative investigation of how the written language of individuals experiencing
suicidal ideation differs from that of a matched non-suicidal population. A purpose-built Suicide Discourse
Corpus (SDC) of approximately 452,000 tokens was compiled from four heterogeneous sources: anonymized
crisis helpline transcripts, Reddit posts from mental health disclosure communities, published first-person
narratives of suicidal crises, and archival farewell notes. A Matched Control Corpus (MCC) of 449,800
tokens from general online discourse was constructed as a baseline. Analytical methods include keyness
analysis (log-likelihood, G²), semantic domain profiling using the UCREL Semantic Analysis System (USAS),
collocational analysis (Mutual Information scoring), and frequency analysis of grammatical and functionalword categories. Findings reveal a statistically robust lexical signature in suicidal discourse marked by: (a)
dramatically elevated pain, suffering, and death-related vocabulary; (b) absolutist and negation-heavy
language reflecting cognitive constriction; (c) a depletion of future-oriented temporal reference and positive
evaluative terms; (d) heightened first-person singular pronoun use alongside reduced social solidarity
vocabulary; and (e) distinctive collocational frames encoding inescapable, internally directed suffering.
These patterns align with major psychological theories of suicide including Shneidman's psychache theory,
Joiner's Interpersonal Theory of Suicide (IPTS), and Beck's cognitive model of hopelessness. Implications for
the design of NLP-assisted early-warning systems, ethical governance of mental health corpus research, and
future multilingual extension of this work are discussed at length
