Predictive linguistic markers of suicidal ideation in autobiographical memory narratives.

BACKGROUND: Suicide claims approximately 700,000 lives annually, yet risk identification remains challenging due to reliance on self-disclosure and limitations of current assessment methods. This study examined whether linguistic analysis of autobiographical memory narratives could predict suicidal ideation. METHODS: In total, 190 participants (137 clinical, 53 community) provided autobiographical memories to twelve emotionally evocative Turkish cue words, analyzed with Linguistic Inquiry and Word Count (LIWC-22) software to extract 90 linguistic features. Full-sample screening identified candidate predictors, interpreted with SHAP analysis. For unbiased validation, feature selection and classification were repeated within a fully nested cross-validation across nine algorithms, with predictors selected independently within each fold and performance compared against a label-permutation null. RESULTS: Discrimination was modest but consistent across all nine algorithms, with the best models reaching an AUC of 0.70, exceeding chance (permutation p < .001). In most models, individuals classified as having suicidal ideation were 1.5 to 1.7 times more likely to actually report it than the base rate. Five linguistic markers, spanning anger-, fear-, and distress-cued memories, were selected in every fold and associated with higher risk. Four remained significant after symptom-severity adjustment, and all five increased with ideation severity. Exploratory SHAP analysis revealed emotion-specific effects, with cognitive-mechanism words in happy memories and impersonal pronouns in fear contexts associated with lower risk. CONCLUSIONS: Linguistic analysis of autobiographical memory content carries a modest but genuine signal for suicidal ideation, and detecting risk from non-suicide-related narratives may help overcome disclosure barriers. Future work should prioritize longitudinal replication, external validation, and clinical translation.