AI-Driven Knowledge Extraction of Therapeutic Rupture Patterns from Counseling Corpora Using LIWC and Machine Learning Classification

Therapeutic alliance ruptures predict treatment outcomes, yet clinicians often struggle to detect them in real time because traditional identification relies on labor-intensive manual coding. The linguistic signatures that distinguish confrontation, withdrawal, and mixed ruptures from intact therapeutic alliances remain insufficiently characterized. The present study analyzed 21,056 words drawn from validated rupture demonstration videos (nine sessions across three rupture types) and master therapist reference sessions (three sessions) using Linguistic Inquiry and Word Count (LIWC-2015) and Random Forest machine learning with leave-one-out cross-validation. Thirteen LIWC variables significantly discriminated rupture types at Bonferroni-corrected thresholds. Third-person pronouns (she/he) showed the strongest effect (G² = 31.85), appearing approximately thirteen times more frequently in withdrawal than confrontation ruptures. Confrontation ruptures exhibited elevated certainty language (2.32% vs. 0.80%), whereas mixed ruptures showed the highest levels of negative emotion language (3.25% vs. 1.61%). Random Forest classifiers achieved perfect accuracy for multi-class rupture classification (100%, F1 = 1.00) and binary rupture detection (100%, AUC-ROC = 1.00), with conversational bigrams such as “it s” (importance = 0.0387) contributing most strongly to classification. Unexpectedly, rupture sessions contained more positive emotion language than reference sessions (4.67% vs. 2.54%), suggesting attempts at relational repair during alliance strain. Given the small sample size and structured nature of the corpus, these classification results should be interpreted as an exploratory demonstration rather than evidence of generalizable predictive performance. These findings identify distinctive linguistic patterns associated with therapeutic alliance ruptures and provide preliminary evidence supporting the feasibility of automated rupture detection, although validation using naturalistic psychotherapy data remains necessary.