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The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP

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arXiv:2608.20353v1 Announce Type: new Abstract: 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 e…

SourcearXiv Computational LinguisticsAuthor: Moustafa Yehia Hassan
The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP
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[Submitted on 16 Jun 2026]

Title:The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP

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Abstract: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

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  • AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
  • arXiv:2608.20353v1 Announce Type: new Abstract: Computational mental health (CMH) classifiers often degrade under distribution shift because human annotators and distant-supervisi…

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