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Leveraging transformer-based language models for psychological distress classification in social media texts
Date Issued
2025
Publisher
IEEE
ISBN
9798331547899
9798331547905
Citation
Amjad, A., Azhar, M., Abdulrahman, R., Dewi, D. A., Jamil, A., & Shabbir, I. (2025). Leveraging transformer-based language models for psychological distress classification in social media texts. In IEEE (Ed.). The proceedings of 2025 international conference on emerging research in computational science (ICERCS). 2025 International Conference on Emerging Research in Computational Science (ICERCS), Coimbatore, India (pp. 1-10). IEEE.
Type
Conference Paper
Abstract
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.
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