[2605.01017] Psychologically Potent, Computationally Invisible: LLMs Generate Social-Comparison Triggers They Fail to Detect
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Computer Science > Computation and Language
arXiv:2605.01017 (cs)
[Submitted on 1 May 2026]
Title:Psychologically Potent, Computationally Invisible: LLMs Generate Social-Comparison Triggers They Fail to Detect
Authors:Hua Zhao, Jiapei Gu, Michelle Mingyue Gu
View a PDF of the paper titled Psychologically Potent, Computationally Invisible: LLMs Generate Social-Comparison Triggers They Fail to Detect, by Hua Zhao and 1 other authors
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Abstract:We introduce Xiaohongshu Social Comparison Reader Elicitation (XHS-SCoRE), a reader-grounded benchmark for detecting if a text-only Xiaohongshu (RedNote) post elicits UPWARD, DOWNWARD, or NEUTRAL/no clear social comparison from a first-person reader perspective. The task targets a socially meaningful relational signal that is behaviorally real yet not reducible to sentiment. Across prompted LLM classifiers and supervised Chinese encoder baselines, we find a consistent mismatch between generation fluency and reliable detection ability: the signal is textually learnable in-domain, but not robustly accessible to prompt-based classification. Prompted LLM classifiers exhibit stable, interpretable failure modes, especially neutralization of comparison-triggering posts and model-specific directional skew. A controlled pilot further shows that LLM-generated Xiaohongshu-style posts can shift perceived standing and comparison-related affect even when prompt-based detection of the same construct remains fragile. XHS-SCoRE contributes both a benchmark for reader-grounded comparison detection and a diagnostic framework for studying when socially meaningful relational cues remain only partially visible to prompt-based inference.
Comments: 20 pages, preprint
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.01017 [cs.CL]
(or arXiv:2605.01017v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.01017
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hua Zhao [view email] [v1] Fri, 1 May 2026 18:28:10 UTC (7,174 KB)
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