@inproceedings{lan-etal-2025-depression,
title = "Depression Detection on Social Media with Large Language Models",
author = "Lan, Xiaochong and
Han, Zhiguang and
Cheng, Yiming and
Sheng, Li and
Feng, Jie and
Gao, Chen and
Li, Yong",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.151/",
pages = "2155--2171",
ISBN = "979-8-89176-333-3",
abstract = "Limited access to mental healthcare resources hinders timely depression diagnosis, leading to detrimental outcomes.Social media platforms present a valuable data source for early detection, yet this task faces two significant challenges: 1) the need for medical knowledge to distinguish clinical depression from transient mood changes, and 2) the dual requirement for high accuracy and model explainability.To address this, we propose DORIS, a framework that leverages Large Language Models (LLMs).To integrate medical knowledge, DORIS utilizes LLMs to annotate user texts against established medical diagnostic criteria and to summarize historical posts into temporal \textit{mood courses.}These medically-informed features are then used to train an accurate Gradient Boosting Tree (GBT) classifier.Explainability is achieved by generating justifications for predictions based on the LLM-derived symptom annotations and mood course analyses.Extensive experimental results validate the effectiveness as well as interpretability of our method, highlighting its potential as a supportive clinical tool."
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<abstract>Limited access to mental healthcare resources hinders timely depression diagnosis, leading to detrimental outcomes.Social media platforms present a valuable data source for early detection, yet this task faces two significant challenges: 1) the need for medical knowledge to distinguish clinical depression from transient mood changes, and 2) the dual requirement for high accuracy and model explainability.To address this, we propose DORIS, a framework that leverages Large Language Models (LLMs).To integrate medical knowledge, DORIS utilizes LLMs to annotate user texts against established medical diagnostic criteria and to summarize historical posts into temporal mood courses.These medically-informed features are then used to train an accurate Gradient Boosting Tree (GBT) classifier.Explainability is achieved by generating justifications for predictions based on the LLM-derived symptom annotations and mood course analyses.Extensive experimental results validate the effectiveness as well as interpretability of our method, highlighting its potential as a supportive clinical tool.</abstract>
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%0 Conference Proceedings
%T Depression Detection on Social Media with Large Language Models
%A Lan, Xiaochong
%A Han, Zhiguang
%A Cheng, Yiming
%A Sheng, Li
%A Feng, Jie
%A Gao, Chen
%A Li, Yong
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F lan-etal-2025-depression
%X Limited access to mental healthcare resources hinders timely depression diagnosis, leading to detrimental outcomes.Social media platforms present a valuable data source for early detection, yet this task faces two significant challenges: 1) the need for medical knowledge to distinguish clinical depression from transient mood changes, and 2) the dual requirement for high accuracy and model explainability.To address this, we propose DORIS, a framework that leverages Large Language Models (LLMs).To integrate medical knowledge, DORIS utilizes LLMs to annotate user texts against established medical diagnostic criteria and to summarize historical posts into temporal mood courses.These medically-informed features are then used to train an accurate Gradient Boosting Tree (GBT) classifier.Explainability is achieved by generating justifications for predictions based on the LLM-derived symptom annotations and mood course analyses.Extensive experimental results validate the effectiveness as well as interpretability of our method, highlighting its potential as a supportive clinical tool.
%U https://aclanthology.org/2025.emnlp-industry.151/
%P 2155-2171
Markdown (Informal)
[Depression Detection on Social Media with Large Language Models](https://aclanthology.org/2025.emnlp-industry.151/) (Lan et al., EMNLP 2025)
ACL
- Xiaochong Lan, Zhiguang Han, Yiming Cheng, Li Sheng, Jie Feng, Chen Gao, and Yong Li. 2025. Depression Detection on Social Media with Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2155–2171, Suzhou (China). Association for Computational Linguistics.