K-SENSE: Knowledge-Guided AI for Mental Health Detection on Social Media
Researchers have just rolled out a new system named K-SENSE, which stands for Knowledge-guided Self-augmented Encoder for Neuro-Semantic Evaluation of Mental Health. This innovative framework analyzes social media text to detect signs of stress and depression. It cleverly combines commonsense knowledge from the COMET model with techniques for self-augmentation and contrastive training. K-SENSE operates through a three-part encoding process that gathers inferential commonsense insights across five mental health dimensions. It effectively navigates challenges like figurative language and subtle emotional hints in user content. This work, aimed at improving early detection in computational psychiatry and natural language processing, is available on arXiv under the code 2604.23493.
Key facts
- K-SENSE is a framework for detecting stress and depression from social media text.
- It uses external commonsense knowledge from the COMET model.
- The framework employs self-augmentation and contrastive training.
- It has a three-stage encoding pipeline.
- The first stage extracts inferential commonsense knowledge across five mental state dimensions.
- It addresses figurative language, implicit emotional expression, and noise.
- Existing approaches typically handle psychological reasoning and representation robustness separately.
- The paper is on arXiv with ID 2604.23493.
Entities
Institutions
- arXiv