arXiv:2606.12418cs.CYcs.AI2026-06

用大模型算命:研究中国人如何在社交平台用AI占卜

Divination by Prompt: LLM-Mediated Xuanxue on Chinese Social Media

  • 通过分析上万条帖子和访谈,发现用户用大模型解决情感、求职等实际问题
  • 多数人认为结果准确,靠的是个人经历匹配和事后验证,体现心理偏见
  • 用户主动优化提示词,形成新型占卜协作模式,适合对AI文化现象感兴趣者

大型语言模型的普及催生了一种新文化实践:用对话式AI进行占卜。本文首次系统研究了中文社交媒体中基于大模型的玄学占卜现象。结合混合方法,分析了小红书上超过23000条帖子与评论,并对32名用户及专业占卜师进行了半结构化访谈。用户主要就恋爱、职业、考试和游戏抽卡等现实问题向大模型咨询,路径包括病毒传播带来的好奇和零成本试用,以及不确定性下的焦虑驱动。关键特征是用户参与提示词优化,成为主动的提示工程师。在表达明确立场的评论中,感知有效性倾向积极,'准确性'常以个人经历契合度和事后确认来解释,符合巴纳姆效应与确认偏误。用户还发展出重复测试和跨模型对比等验证方式。专业占卜师则认为大模型缺乏'灵力',反映其本体论立场与经济边界维护。研究揭示参与者在科学与神秘框架间调和的张力。结合人类学与认知进化理论,我们认为大模型占卜保留了传统占卜的核心功能,同时引入可扩展性、可重复性和提示驱动的共生产机制,重塑了占卜权威的建构与评价方式。

原文摘要 · Abstract (English)

The rapid proliferation of large language models (LLMs) has produced a striking cultural practice: using conversational AI for divination. This paper offers one of the first systematic studies of LLM-mediated divination in the context of Xuanxue, an internet-native umbrella term for mystical and spiritual practices on Chinese social media. Using a mixed-methods design, we analyze 23000+ posts and comments from Xiaohongshu and conduct 32 semi-structured interviews with users and professional diviners. Users primarily consult LLMs about pragmatic concerns - romantic relationships, careers, exams, and in-game gacha draws - via two intersecting pathways: trend-driven curiosity enabled by viral visibility and zero-cost access, and event-driven anxiety under conditions of uncertainty. A defining feature is collaborative prompt refinement, which turns users into active prompt engineers. Among commenters expressing a clear stance, perceived efficacy skews positive, with "accuracy" often justified through biographical fit and retrospective confirmation, consistent with Barnum and confirmation bias. Users also develop verification practices such as repeated trials and cross-model comparison. Professional diviners, by contrast, portray LLMs as lacking the "spiritual power" required for genuine divination, reflecting both ontological commitments and economic boundary-work. We also show how participants navigate tensions between scientific and metaphysical frames when interpreting AI-generated readings. Situating these findings in anthropological and cognitive-evolutionary theories of divination, we argue that LLM divination preserves core functions of traditional practice while introducing scalability, repeatability, and prompt-driven co-production that reshape how divinatory authority is constructed and evaluated.

AI占卜社会媒体认知偏误提示工程

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