RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Social Media
This work addresses the challenge of temporal dynamics in AIGT detection for social media researchers, though it is incremental as it builds on existing detection methods with a new dataset and framework.
The authors tackled the problem of detecting AI-generated text (AIGT) in social media by introducing RedNote-Vibe, a 5-year longitudinal dataset from Xiaohongshu, and proposing PLAD, an interpretable detection framework that achieves superior performance and reveals relationships between linguistic features and user engagement.
The proliferation of Large Language Models (LLMs) has led to widespread AI-Generated Text (AIGT) on social media platforms, creating unique challenges where content dynamics are driven by user engagement and evolve over time. However, existing datasets mainly depict static AIGT detection. In this work, we introduce RedNote-Vibe, the first longitudinal (5-years) dataset for social media AIGT analysis. This dataset is sourced from Xiaohongshu platform, containing user engagement metrics (e.g., likes, comments) and timestamps spanning from the pre-LLM period to July 2025, which enables research into the temporal dynamics and user interaction patterns of AIGT. Furthermore, to detect AIGT in the context of social media, we propose PsychoLinguistic AIGT Detection Framework (PLAD), an interpretable approach that leverages psycholinguistic features. Our experiments show that PLAD achieves superior detection performance and provides insights into the signatures distinguishing human and AI-generated content. More importantly, it reveals the complex relationship between these linguistic features and social media engagement. The dataset is available at https://github.com/testuser03158/RedNote-Vibe.