SleepDepNet: A Multi-Task Transformer Framework for Assessing Sleep Quality and Depression Risk from Social Media Narratives
Kumar, A.; Sangwan, S. R.; Sharma, A.
Show abstract
The bidirectional relationship between sleep disturbances and depression presents a serious challenge for digital mental health research and intervention. This study introduces SleepDepNet, a transformer-based multi-task learning model designed to assess sleep quality and depressive sentiment simultaneously from user-generated narratives on Reddit. Leveraging a large, custom-labelled dataset drawn from subreddits such as r/depression, r/sleep, r/mentalhealth, and r/insomnia, SleepDepNet integrates attention mechanisms, sentiment and emotion analysis, and topic modelling to capture linguistic markers of emotional exhaustion and disordered sleep. The model achieves strong performance (F1-scores of 0.89 for sleep quality and 0.86 for depressive sentiment), while its attention-based interpretability supports transparent clinical insight. The proposed SleepDepScore, a unified metric derived from both tasks, offers a scalable approach to digital risk stratification and mental health triage. These results demonstrate SleepDepNets potential for real-world deployment in AI-driven mental health monitoring and personalized digital care.
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