arXiv cs.CL· Nada Xu, Xueqin Chen, Qiang Gao, Hongru Liang, Wenqiang Lei·· 12 小时前AI 评分22
UMer：从社交媒体痕迹估计抑郁严重程度的用户特定心理证据推理框架
Words Are the Voice of the Mind: Depression Severity Estimation from Social Traces via Mental-Guided Evidence Reasoning
AI 导读
UMer 是一个基于社交媒体痕迹的用户特定心理证据推理框架，用于估计抑郁严重程度。它先从历史社交痕迹中蒸馏用户特定的心理先验，再通过量表引导的多查询检索构建证据记忆，最后以从粗到细的证据感知推理区分直接与间接证据并预测条目级分数。在真实基准上，UMer 性能优于现有基线，并提供有证据支撑的可解释性。
正文
Abstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.33586 [cs.CL] |
| (or arXiv:2610.33586v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33586 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nada Xu [view email]
[v1]
Sat, 24 Oct 2026 10:28:49 UTC (1,012 KB)
来源：arXiv cs.CL · arxiv.org