分歧假说：揭示心理健康自然语言处理中的词汇干扰与标签偏差
The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP
- Doha Institute for Graduate Studies（多哈高等研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出TSS诊断框架与DoD统计量，揭示心理健康NLP分类器的词汇干扰与标签偏差，可标记特定标签源的捷径学习，为相关模型审计提供支撑。
AI中文摘要：
计算心理健康（CMH）分类器在分布偏移下常性能下降，原因是人工标注者与远程监督流程会奖励不同的语言信号。我们提出TSS（Triple-Stream Stress probe，三流压力探测仪），这是一种多通道诊断框架，将文本分解为：(A)词汇字符n元组、(B)小型、主要为无内容的形态句法通道、(C)含154个特征的心理语言学风格通道。在四个英文数据集（N=12906）上，TSS揭示了词汇干扰效应：向风格通道添加词汇特征会降低人工标注数据的Macro-F1（平均下降0.072，p<10^-4），但对自动标注数据无此影响。我们提出分歧度（DoD），一种改编自计量经济学的双重差分统计量，用于标签源审计，兼具实例级自助法推断；核心估计值为DoD(BC-A)=0.0374，95%置信区间[0.0097,0.0651]，p=0.0032。仅针对Twitter的平台分层DoD（消除Reddit与Twitter的对比）通过自助法推断重现该模式：DoD-Tw(BC-A)=+0.096（p<0.001），DoD-Tw(AC-A)=-0.089（p<0.001）。干预性掩码（pos_only）在破坏人工数据集内容词后，保留了约95-99%的C通道性能，表明风格通道不主要依赖词汇表层形式。TSS被定位为诊断审计框架，而非临床筛查工具：它在提出泛化主张前，标记特定标签源的捷径学习。
英文摘要：
Computational mental health (CMH) classifiers often degrade under distribution shift because human annotators and distant-supervision pipelines reward different linguistic signals. We introduce TSS (Triple-Stream Stress probe), a multi-channel diagnostic framework that decomposes text into (A) lexical character n-grams, (B) a small, mostly content-free morpho-syntactic channel, and (C) a 154-feature psycholinguistic style channel. Across four English datasets (N=12,906), TSS reveals a lexical interference effect: adding lexical features to the style channel reduces Macro-F1 on human-labeled data (mean drop 0.072, p<10^-4) but not on auto-labeled data. We propose Degree of Divergence (DoD), a difference-in-differences statistic adapted from econometrics for label-source auditing, with instance-level bootstrap inference; the headline estimate is DoD(BC-A) = 0.0374, 95% CI [0.0097, 0.0651], p=0.0032. A platform-stratified Twitter-only DoD (which removes the Reddit vs. Twitter contrast) reproduces the pattern with bootstrap inference: DoD-Tw(BC-A) = +0.096 (p<0.001) and DoD-Tw(AC-A) = -0.089 (p<0.001). Interventional masking (pos_only) retains ~95-99% of Channel C's performance after destroying content words on human datasets, indicating that the style channel does not rely primarily on lexical surface form. TSS is positioned as a diagnostic audit framework, not a clinical screening tool: it flags label-source-specific shortcut learning before generalization claims are made.
