LLM生成的社交动态易引发比较心理，却无法自检。
Psychologically Potent, Computationally Invisible: LLMs Generate Social-Comparison-Eliciting Posts They Fail to Detect
- 构建读者视角的社交比较检测基准XHS-SCoRE，识别上行、下行与中性比较
- 模型生成内容引发比较感，但提示词分类器对这类信号检测失败
- 揭示大模型在社交关系信号感知上的盲区，适合研究人机交互与社会影响
我们提出 Xiaohongshu 社交比较诱发检测基准（XHS-SCoRE），从第一人称读者视角判断纯文本小红书帖子是否引发向上、向下或中性/无明确社交比较。该任务捕捉一种非情感化的、行为真实的社交关系信号。在提示词驱动的 LLM 分类器与监督式中文编码器中，均发现生成-检测不一致：信号在域内可被文本学习，但难以通过提示词分类稳健识别。提示词 LLM 分类器表现出稳定失效，尤其将比较性内容中性化，并存在模型特异性方向偏差。受控预实验显示，大模型生成的小红书风格内容即使未被提示词检测出，仍可改变用户自我定位与比较相关情绪。XHS-SCoRE 提供读者导向的比较检测基准与诊断框架，用于分析社交意义关系线索在提示推理中的部分可见性。
原文摘要 · Abstract (English)
We introduce Xiaohongshu Social Comparison Reader Elicitation (XHS-SCoRE), a reader-grounded benchmark for detecting whether text-only Xiaohongshu (RedNote) posts elicit Upward, Downward, or Neutral/no clear social comparison from a first-person reader perspective. The task targets a socially meaningful relational, behaviorally real signal not reducible to sentiment. Across prompted LLM classifiers and supervised Chinese encoders, we find a consistent generation-detection mismatch: the signal is textually learnable in-domain, but not robustly accessible to prompt-based classification. Prompted LLM classifiers show stable failures, especially neutralization of comparison-eliciting posts and model-specific directional skew. A controlled pilot 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 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.
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