用弱先验引导专家路由，提升社交媒体抑郁检测精准度
WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection
- 基于大模型提取用户语义先验，动态分配至匹配的专家
- 在中英文数据集上均超越现有基线，尤其改善非自述用户识别
- 适合关注个性化心理健康筛查与可解释性模型的研究者
在线社交平台内容为早期抑郁症筛查提供了可扩展信号。现有研究多通过风险帖筛选、症状定位和临床特征构建来增强预分类证据，但常依赖单一检测器做出最终判断，忽视用户在筛选后表达抑郁风险的异质性。单体分类器需对异构用户取平均，可能稀释局部证据，导致误判，尤其针对不主动披露的用户。为此，本文提出弱先验引导的密集专家混合框架（WPG-MoE），基于共享的大语言模型（LLM）主干构建。WPG-MoE利用用户级弱语义先验，软性路由用户至适配不同证据结构的专家。该过程被形式化为使用特权信息学习（LUPI）：训练时利用LLM提取的结构化证据指导路由，推理时仅保留患者健康问卷-9（PHQ-9）模板筛查与可部署主干。在中文和英文数据集上的实验表明，WPG-MoE优于强基线，且路由行为具有可解释性。
原文摘要 · Abstract (English)
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.
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