Rising mental health issues among adolescents have increased interest in automated approaches for detecting early signs of psychological distress in digital text. One important focus is the identification of cognitive distortions – irrational thought patterns – because of their role in aggravating mental distress, and early detection may enable timely, low cost interventions. While prior work has focused on English data, we present a first in-depth study of cross lingual and cross register generalization for cognitive distortion detection, using forum posts written by Dutch adolescents. We frame the task at two levels: (1) detecting whether a post contains a cognitive distortion, and (2) identifying the specific text span that expresses it. Our findings show that domain adaptation methods perform best for post-level detection, while a simpler technique – sentence embeddings with a classifier – outperforms more complex models for span identification. Results show predicting cognitive distortions in text is challenging, and highlight how changes in language and writing style can significantly impact performance.

Suicide is a leading cause of death, yet predicting it remains a significant challenge. Risk factors such as depression or substance use are commonly used for prediction, but their predictive performance is often only slightly better than chance. Additionally, many cases go undetected due to a lack of contact with mental health services. Social media, however, offers a unique opportunity, as people often share their thoughts and struggles online in real time. In this work, we propose a novel task and method to approach it: predicting suicidal ideation and behavior (SIB) before a user ever expresses it on an online forum. This predictive framing, where no self-disclosure is used as input at any stage, remains largely unexplored in the suicide prediction literature. Our model, Early-SIB, achieves a balanced accuracy of 0.73 for predicting future SIB on a Dutch youth forum, demonstrating that such tools can offer a meaningful addition to traditional methods.