@inproceedings{agarwal-etal-2025-redepress,
title = "{R}e{D}epress: A Cognitive Framework for Detecting Depression Relapse from Social Media",
author = "Agarwal, Aakash Kumar and
Bhattacharjee, Saprativa and
Rastogi, Mauli and
Jacob, Jemima S. and
Banerjee, Biplab and
Gupta, Rashmi and
Bhattacharyya, Pushpak",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1758/",
doi = "10.18653/v1/2025.emnlp-main.1758",
pages = "34652--34670",
ISBN = "979-8-89176-332-6",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media
%A Agarwal, Aakash Kumar
%A Bhattacharjee, Saprativa
%A Rastogi, Mauli
%A Jacob, Jemima S.
%A Banerjee, Biplab
%A Gupta, Rashmi
%A Bhattacharyya, Pushpak
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F agarwal-etal-2025-redepress
%X 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.
%R 10.18653/v1/2025.emnlp-main.1758
%U https://aclanthology.org/2025.emnlp-main.1758/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1758
%P 34652-34670
Markdown (Informal)
[ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media](https://aclanthology.org/2025.emnlp-main.1758/) (Agarwal et al., EMNLP 2025)
ACL