构建了五年期社交平台文本生成动态数据集，揭示人类与AI内容差异。
RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media
- 基于认知心理学设计可解释的AI文本检测框架PLAD
- 发现人类内容在情感共鸣上仍优于AI，但高投入互动中差距缩小
- 少数用户策略性使用AI工具实现更高传播效果，适合研究者参考
我们提出RedNote-Vibe，一个覆盖五年（预大模型时代至2025年7月）的生活方式类社交平台红书（Xiaohongshu）数据集，捕捉内容创作的时间动态，并附带全面的互动指标。为应对该数据集带来的检测挑战，我们提出基于认知心理学的PsychoLinguistic AIGT Detection Framework（PLAD），利用深层心理特征实现鲁棒且可解释的检测。实验表明PLAD表现优异，并揭示以下洞见：(1) 人类内容在情感共鸣领域持续优于AI；(2) AI内容更具同质性且极少产生爆款，但高投入互动中人机差距缩小；(3) 最具启发的是，少数用户通过策略性使用AI工具实现了更高互动效果。数据集已公开于https://github.com/ydli-ai/RedNote-Vibe。
原文摘要 · Abstract (English)
We introduce RedNote-Vibe, a dataset spanning five years (pre-LLM to July 2025) sourced from lifestyle platform RedNote (Xiaohongshu), capturing the temporal dynamics of content creation and is enriched with comprehensive engagement metrics. To address the detection challenge posed by RedNote-Vibe, we propose the \textbf{PsychoLinguistic AIGT Detection Framework (PLAD)}. Grounded in cognitive psychology, PLAD leverages deep psychological signatures for robust and interpretable detection. Our experiments demonstrate PLAD's superior performance and reveal insights into content dynamics: (1) human content continues to outperform AI in emotionally resonant domains; (2) AI content is more homogeneous and rarely produces breaking posts, however, this human-AI gap narrows for arousing higher-investment interactions; and (3) most interestingly, a small group of users who strategically utilize AI tools can achieve higher engagement outcomes. The dataset is available at https://github.com/ydli-ai/RedNote-Vibe
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