Work

Detection of Depression Severity in Social Media Text Using Transformer-Based Models

Year: 2025

Type: article

Abstract: Depression, a serious mental health disorder, requires accurate classification for effective intervention. Existing methods often fail to capture nuanced emotional and linguistic cues, leading to suboptimal classification of depression severity. This study bridges this gap by leveraging content-based approaches (N-grams) and context-based methods (Sentence Transformers), alongside advanced transformer-based models, to classify mild, moderate, and severe depression using text data sourced from Reddit. By demonstrating the effectiveness of modern NLP techniques in capturing subtle contextual variations, this research highlights the potential of transformer-based models to enhance depression severity detection. The proposed framework offers a scalable and adaptable solution for real-world mental health diagnostics and early intervention systems.

Source: Information

DOI: 10.3390/info16020114

Authors: Amna Qasim, Gull Mehak, Nisar Hussain, Alexander Gelbukh, Grigori Sidorov

Institution: Instituto Politécnico Nacional

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,930