@inproceedings{go-etal-2025-xdac,
    title = "{XDAC}: {XAI}-Driven Detection and Attribution of {LLM}-Generated News Comments in {K}orean",
    author = "Go, Wooyoung  and
      Kim, Hyoungshick  and
      Oh, Alice  and
      Kim, Yongdae",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.acl-long.1108/",
    doi = "10.18653/v1/2025.acl-long.1108",
    pages = "22728--22750",
    ISBN = "979-8-89176-251-0",
    abstract = "Large language models (LLMs) generate human-like text, raising concerns about their misuse in creating deceptive content. Detecting LLM-generated comments (LGC) in online news is essential for preserving online discourse integrity and preventing opinion manipulation. However, effective detection faces two key challenges; the brevity and informality of news comments limit traditional methods, and the absence of a publicly available LGC dataset hinders model training, especially for languages other than English. To address these challenges, we propose a twofold approach. First, we develop an LGC generation framework to construct a high-quality dataset with diverse and complex examples. Second, we introduce XDAC ($\textbf{X}$AI-Driven $\textbf{D}$etection and $\textbf{A}$ttribution of LLM-Generated $\textbf{C}$omments), a framework utilizing explainable AI, designed for the detection and attribution of short-form LGC in Korean news articles. XDAC leverages XAI to uncover distinguishing linguistic patterns at both token and character levels. We present the first large-scale benchmark dataset, comprising 1.3M human-written comments from Korean news platforms and 1M LLM-generated comments from 14 distinct models. XDAC outperforms existing methods, achieving a 98.5{\%} F1 score in LGC detection with a relative improvement of 68.1{\%}, and an 84.3{\%} F1 score in attribution. To validate real-world applicability, we analyze 5.24M news comments from Naver, South Korea{'}s leading online news platform, identifying 27,029 potential LLM-generated comments."
}