Leveraging Large Language Models to Map Triggers of Contamination-Related Obsessive-Compulsive Symptoms

Recent advancements in natural language processing (NLP) and large language models (LLMs) offer new avenues for exploring previously under-researched areas in mental health. Their capacity to automatically and meaningfully analyze large-scale text data makes them particularly valuable for studying highly individualized phenomena with clinical relevance, such as triggers of obsessive-compulsive symptoms (OCS), where pattern identification is often challenging. To address this gap, we surveyed 1,495 individuals from the general population about contamination-related obsessive–compulsive symptoms (C-OCS), as well as their triggers and corresponding intensity. Using LLM-based embeddings, we generated a map of key trigger categories for C-OCS, revealing their diversity across ecological domains and varying degrees of semantic similarity. Monte Carlo simulations further showed that individuals frequently reported semantically similar trigger pairs that differed in intensity. These findings provide a basis for further investigations into associative learning processes at the categorial and semantic levels. Such research in turn may enhance understanding of mechanisms involved in the development, maintenance, and treatment of obsessive-compulsive disorders, and may inform novel therapeutic approaches.