ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914051202744320 |
|---|---|
| author | Agarwal, Aakash Kumar Bhattacharjee, Saprativa Rastogi, Mauli Jacob, Jemima S. Banerjee, Biplab Gupta, Rashmi Bhattacharyya, Pushpak |
| author_facet | Agarwal, Aakash Kumar Bhattacharjee, Saprativa Rastogi, Mauli Jacob, Jemima S. Banerjee, Biplab Gupta, Rashmi Bhattacharyya, Pushpak |
| contents | Almost 50% depression patients face the risk of going into relapse. The risk increases to 80% after the second episode of depression. Although, depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored due to the lack of curated datasets and the difficulty of distinguishing relapse and non-relapse users. In this work, we present ReDepress, the first clinically validated social media dataset focused on relapse, comprising 204 Reddit users annotated by mental health professionals. Unlike prior approaches, our framework draws on cognitive theories of depression, incorporating constructs such as attention bias, interpretation bias, memory bias and rumination into both annotation and modeling. Through statistical analyses and machine learning experiments, we demonstrate that cognitive markers significantly differentiate relapse and non-relapse groups, and that models enriched with these features achieve competitive performance, with transformer-based temporal models attaining an F1 of 0.86. Our findings validate psychological theories in real-world textual data and underscore the potential of cognitive-informed computational methods for early relapse detection, paving the way for scalable, low-cost interventions in mental healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17991 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media Agarwal, Aakash Kumar Bhattacharjee, Saprativa Rastogi, Mauli Jacob, Jemima S. Banerjee, Biplab Gupta, Rashmi Bhattacharyya, Pushpak Computation and Language Artificial Intelligence Machine Learning Almost 50% depression patients face the risk of going into relapse. The risk increases to 80% after the second episode of depression. Although, depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored due to the lack of curated datasets and the difficulty of distinguishing relapse and non-relapse users. In this work, we present ReDepress, the first clinically validated social media dataset focused on relapse, comprising 204 Reddit users annotated by mental health professionals. Unlike prior approaches, our framework draws on cognitive theories of depression, incorporating constructs such as attention bias, interpretation bias, memory bias and rumination into both annotation and modeling. Through statistical analyses and machine learning experiments, we demonstrate that cognitive markers significantly differentiate relapse and non-relapse groups, and that models enriched with these features achieve competitive performance, with transformer-based temporal models attaining an F1 of 0.86. Our findings validate psychological theories in real-world textual data and underscore the potential of cognitive-informed computational methods for early relapse detection, paving the way for scalable, low-cost interventions in mental healthcare. |
| title | ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.17991 |