Depression severity estimation is crucial for mental health assessment and intervention, yet traditional approaches depend on clinical interviews or self-report questionnaires that require users' active participation. With the widespread use of online social media, users leave rich social traces that may reflect their mental states, motivating the task of social media-based depression severity estimation. Existing efforts have progressed from textual-feature-based classification to LLM-based questionnaire completion over standardized scale items, yet they still fall short in user-specific mental priors modeling, diversified semantic retrieval, and fine-grained reasoning over mental evidence. To address these limitations, we propose UMer, a user-specific mental evidence reasoning framework for social traces-based depression severity estimation. UMer first distills a user-specific mental prior from historical social traces to capture the user's psychological tendencies, behavioral patterns, and self-expression style. It then performs scale-guided multi-query retrieval to construct evidence memories from item-relevant posts. Finally, it conducts coarse-to-fine evidence-aware reasoning to distinguish direct and indirect evidence and predict item-level scores with interpretable rationales. Extensive experiments on real-world benchmarks demonstrate that UMer not only achieves competitive performance over existing baselines, but also provides evidence-grounded interpretability and generalizes to other scale-based tasks.
Figures & tables
Figure 1: Existing Efforts vs. UMer.
Figure 2: Overview of UMer, a three-stage LLM-based agentic framework. The model progressively constructs the user-specific mental prior from historical social traces, retrieves item-relevant posts to construct scale-aware mental evidence memories, and performs coarse-to-fine evidence-aware reasoning for interpretable item-level scoring.
Questionnaire Metrics
Symptom Metrics
Collection
Model
DCHR % ( ↑ )
ADODL % ( ↑ )
AHR % ( ↑ )
ACR % ( ↑ )
eRisk 2020
BioInfo ‡
30.00
76.01
38.30
69.21
iLab ‡
27.14
81.70
37.07
69.41
RELAI ‡
34.29
83.15
36.39
68.32
Sense2Vec ‡
37.14
82.61
38.97
70.10
Voting †
47.14
85.33
35.24
67.41
Table 1: Depression severity estimation on eRisk 2020 and eRisk 2021. † : uses manual annotations; ‡ : involves task-specific model training. The best results are highlighted in bold , and the second-best results are underlined .
Figure 3: The score distribution between proprietary LLM and Open-weight LLM
Dataset
Objective Accuracy
Completeness
Subjective Reasonableness
eRisk-Avg
4.53
4.02
4.14
Table 2: Average scores assigned by three human annotators for each mental prior quality metric.
Configuration
MAE ( ↓ )
Symptom Metrics
Questionnaire Metrics
AHR % ( ↑ )
ACR % ( ↑ )
ADODL % ( ↑ )
DCHR % ( ↑ )
UMer
0.8082
41.97
73.77
84.13
40.00
w/o Su
0.8707
38.57
70.97
81.72
28.57
w/o SMR
0.8361
40.27
72.13
82.56
31.43
w/o EaR
0.8721
39.12
70.93
81.68
38.57
Table 3: Ablation study on eRisk 2020.
Figure 4: Generalization evaluation on eRisk 2022 and eRisk 2023 EDE-Q scoring tasks. Lower values indicate better performance.
Appendix figures & tables9 assets
Supplementary material from the paper’s appendix.
Appendix
Figure A1: Content attention analysis. "Mixed" denotes reasoning that jointly relies on information from both the mental prior and retrieved posts.
Retriever
MAE ↓
Symptom Metrics
Questionnaire Metrics
AHR % ( ↑ )
ACR % ( ↑ )
ADODL % ( ↑ )
DCHR % ( ↑ )
BGE-M3
0.8082
41.97
73.77
84.13
40.00
GTE multilingual base
0.8354
38.91
72.15
83.13
44.29
Qwen3 embedding 0.6B
0.8517
39.66
71.61
82.18
38.29
Appendix
Table A1: Performance of different embedding encoders.
Configuration
MAE ↓
Symptom Metrics
Questionnaire Metrics
AHR % ( ↑ )
ACR % ( ↑ )
ADODL % ( ↑ )
DCHR % ( ↑ )
top-25
0.8082
41.97
73.77
84.13
40.00
top-10
0.8422
39.66
71.93
82.22
30.00
top-15
0.8395
39.46
72.02
82.45
28.57
top-20
0.8388
39.18
72.04
82.93
32.86
top-30
0.8279
39.93
72.40
82.84
34.29
Appendix
Table A2: Performance of different retrieval depths.
Figure A2: Case study for mental prior
Figure A3: Prompt template for constructing the accumulated mental prior from posts.
Figure A4: Prompt template for multi-query construction.
Figure A5: Prompt template for structured reasoning and final item-level self-rating simulation.
Figure A6: Prompt template for key-LLM-based query generation.
Figure A7: Prompt template for Generalization ability Experiment.
Online social media posts provide scalable signals for early depression screening, and recent studies mainly improve pre-classification evidence through risk-post selection, symptom grounding, and clinically informed feature construction. However, these screening-stage designs often leave final decisions to a single detector, overlooking how users heterogeneously express depressive risk after screening. A monolithic classifier must average across heterogeneous users, which may dilute localized evidence and cause misclassification, especially for non-self-disclosing users. To address this issue, we propose WPG-MoE, a weak-prior-guided dense mixture-of-experts framework built on a shared large language model (LLM) backbone. WPG-MoE derives user-level weak semantic priors to softly route users to experts matched to different evidence layouts. We formulate this process as learning using privileged information (LUPI): rich LLM-extracted structured evidence guides training-time routing, while inference retains only Patient Health Questionnaire-9 (PHQ-9) template screening and the deployable backbone. Experiments on Chinese and English datasets show that WPG-MoE outperforms strong baselines with interpretable routing behavior.
Xian Li, Yuanhe Tian, Yang Yang +2
University of Electronic Science and Technology of China · Zhongguancun Academy · University of Science and Technology of China
Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention. Validated instruments such as the Patient Health Questionnaire-9 (PHQ-9) support symptom monitoring at scale, but real-world completion rates are low, introducing response bias and systematic missingness. Passive approaches that infer severity from routinely generated data could close this gap. We address this by predicting PHQ-9 total scores directly from transcripts of conversations between users and an AI mental health application, requiring only conversation text and no additional clinical data. We fine-tune a Qwen3.5-27B backbone with a regression head, augment 3,111 ground-truth labels with pseudolabels generated by a reasoning model (Claude Opus) and iteratively trained intermediate models, for a combined dataset of 6,283 users. On a held-out test set of 842 users, our best model achieves MAE = 2.6, RMSE = 4.0, Pearson r = 0.80, and AUC = 0.91 at the PHQ-9 >= 10 clinical threshold. We also find AUC > 0.87 at every severity threshold from PHQ-9 >= 3 to PHQ-9 >= 24, demonstrating that the model captures depression severity across the full clinical spectrum. This work opens the door to passive, continuous symptom monitoring in AI mental health platforms, without requiring users to complete self-report measures.
Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The proliferation of social media platforms provides a rich source of naturalistic linguistic signals for the automated monitoring of psychological well-being. In this work, we propose a system based on Large Language Models (LLMs) for depression risk assessment in Reddit posts, through multi-label classification of eight depression-associated emotions and the computation of a weighted severity index. The method is evaluated in a zero-shot setting on the annotated DepressionEmo dataset (~6,000 posts) and applied in-the-wild to 469,692 comments collected from four subreddits over the period 2024-2025. Our best model, gemma3:27b, achieves micro-F1 = 0.75 and macro-F1 = 0.70, results competitive with purpose-built fine-tuned models (BART: micro-F1 = 0.80, macro-F1 = 0.76). The in-the-wild analysis reveals consistent and temporally stable risk profiles across communities, with marked differences between r/depression and r/anxiety. Our findings demonstrate the feasibility of a cost-effective, scalable approach for large-scale psychological monitoring.
Giorgia Gulino, Manuel Petrucci
1Guglielmo Marconi University, Department of Human Sciences, Rome, Italy