@inproceedings{li-zhang-2025-linguistic,
    title = "Linguistic Differences between {AI} and Human Comments in {W}eibo: Detect {AI}-Generated Text through Stylometric Features",
    author = "Li, Ziqi  and
      Zhang, Qi",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-1.64/",
    pages = "842--851",
    abstract = "``LLM-enhanced social robots (LLM-Bots) generate responses similar to human interactions and pose risks to social media platforms. Distinguishing AI-generated texts (AIGTs) from human-written content is important for mitigating these threats. However, current AIGT detection technologies face limitations in social media contexts, including inadequate performance on shorttexts, poor interpretability, and a reliance on synthetic datasets. To address these challenges, this study first constructs a social media dataset composed of 463,382 Weibo comments to capture real-world interactions between LLM-Bots and human users. Second, a stylo metric feature set tailored to Chinese social media is developed. We conduct a comparative analysis of these features to reveal linguistic differences between human-written and AI-generated comments. Third,we propose a lightweight stylo metric feature-based self-attention classifier (SFSC). This model achieves a strong F1-score of 91.8{\%} for detecting AI-generated short comments in Chinese while maintaining low computational overhead. Additionally, we provide interpretable criteria for the SFSC in AIGT detection through feature importance analysis. This study advances detection forAI-generated short texts in Chinese social media.''"
}