PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media
While exist?ing methods have achieved promising results in automated depression detection, they face two key limitations: (1) noise in post screening: symptom-based semantic screening is easily misled by superficial lexical patterns, thereby introducing false-positive posts; (2) weak op?timization of the post encoder: relying solely on coarse user-level supervision signals pre?vents models from capturing depressive cues within individual posts.
ProofPaper ↗
Key points
- Depression is a prevalent mental disorder that affects millions worldwide, with early detection crucial for effective intervention.
- To address these limita?tions, we propose PAT, a Personality-driven Augmentation and Transfer learning frame?work.
- In the first phase, PAT optimizes a post?level encoder via a fine-grained contrastive learning objective, utilizing LLM-based data augmentation driven by classic personality the?ories to alleviate data scarcity.
- Furthermore, PAT delivers multi?granular interpretability by tracing post-level mental-state trajectories and highlighting key symptoms, thereby offering reliable diagnostic evidence for clinical practice.
Sources (1)
- [1]PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social MediaarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 04:30 PM
While exist?ing methods have achieved promising results in automated depression detection, they face two key limitations: (1) noise in post screening: symptom-based semantic screening is easily misled by superficial lexical patterns, thereby introducing false-positive posts; (2) weak op?timization of the post encoder: relying solely on coarse user-level supervision signals pre?vents models from ca
Depression is a prevalent mental disorder that affects millions worldwide, with early detection crucial for effective intervention.
Extractive summary: sentences quoted from the sources.
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