Capturing Troubled Thoughts: Context-Aware Cognitive Distortion Detection and Classification in Dutch Adolescent Forum Posts

Abstract

Mental health problems among adolescents continue to rise, with growing interest in identifying early signs of psychological distress, including cognitive distortions (CDs). CDs are negatively biased patterns of thinking associated with conditions such as depression and anxiety. As CDs are primarily expressed through language, Natural Language Processing (NLP) methods have increasingly been explored for their automatic detection and classification, potentially enabling earlier intervention. This work provides a dataset of Dutch adolescent forum posts from the Kindertelefoon, annotated by professionals with a background in Cognitive Behavioral Therapy (CBT). The dataset is utilized for sentence-level CD detection and classification, exploring the role of local context (the surrounding post) and longitudinal context (previous posts from the same user) on model performance. Experiments compare zero-shot prompting methods with finetuned transformer-based models, using different approaches to incorporate context. Results show that incorporating context does not consistently improve CD detection or classification performance in naturalistic adolescent forum posts.