Browsing by Research Output - Author "Abdulrahman, Raja"

The rise in the prevalence of social media platforms has necessitated the development of automated software to detect early warning signs of psychological distress in user-generated content. Despite the computational model’s potential, the approach remains primarily geared towards single-task classification, thereby overlooking the multifaceted nature of assessing clinical distress. In this paper, the new transformer-based multi-task learning approach is presented to classify psychological distress across three essential domains: severity, primary clinical domain, and urgency. Notably, the new approach integrates clinical feature maps and the temporal analysis component to provide evidence of symptom progression. The model, tested and validated on a carefully analyzed dataset of 15,000 Reddit posts, achieves state-of-the-art performance, with an accuracy of 85.7% and an AUC-ROC of 0.932, thereby setting new benchmarks for contemporary single-task and multi-task models. Notably, the model’s performance in detecting the urgency level remains particularly strong, an essential component of risk assessment.