Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk

Sensitive content warning: This paper contains sensitive content related to suicide. Psychological risk factors for suicide have been extensively studied for decades. However, combining explainable theory with modern data-driven language modeling approaches is nontrivial. Here, we propose and evaluate methods for identifying language patterns indicative of suicide risk by combining theory-driven suicidal archetypes with language model-based and relative entropy-based approaches. Archetypes are based on prototypical statements that evince risk of suicidality while relative entropy considers the difference between how probable the risk-familiar and risk-unfamiliar models find user language. Each approach performed well individually; combining the two strikingly improved performance, yielding our combined system submission with a BERTScore Recall of 0.906. Further, we find diagnostic language is distributed unevenly in posts, with titles containing substantial risk evidence. We conclude that a union between theory- and data-driven methods is beneficial, outperforming more modern prompt-based methods.