Signs of Struggle: Spotting Distorted Thoughts in Social Media Text

Abstract

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.