Lightweight advanced deep-learning models for stress detection on social media

Abstract

Nowadays, stress reveals itself as a ubiquitous presence, manifesting in novel forms in our modern daily life. Indeed, digital platforms and social media collect various impressions, reactions, and feelings that could provide valuable real-time sentiment data. Nevertheless, understanding stress and mental states among people is difficult because it relies on self-reporting and detecting related expressions, statements, and articulations. In this paper, we consider extracting nuanced insights and stress expressions from Reddit and Twitter posts using lightweight advanced deep-learning methods and Bidirectional Encoder Representations from Transformers (BERT) embeddings. Our findings highlight the potency of transformer BERT models, whether utilized as embedding feature extractors or as text sentiment classifiers. Moreover, the proposed lightweight deep architectural models promoted the field of stress detection in social media, achieving high classification performance. Practically, the BERT Electra model reached 85.67% accuracy on the small Reddit dataset, while our Convolutional Neural Network (CNN) model obtained 97.62% on the large Twitter dataset. Our contributions are not only restricted to the scientific understanding of stress but also extend to the well-being of individuals and global mental health.