Large language models (LLMs) can generate human-like online comments at scale, raising concerns that coordinated AI campaigns could manipulate public opinion. While prior work has shown that single AI messages can persuade individual readers, it remains untested whether coordinated multi-agent LLM campaigns, in which multiple agents jointly plan, divide roles, and manage visibility, can shift reader opinions in news comment sections, and whether per-comment AI labels can mitigate such influence. We address these questions through a controlled experiment with 1,000 U.S. adults reading news articles on a Reddit-like platform. Each participant was randomly assigned to one of six conditions, crossing three comment sources (human, independently generated AI, or coordinated multi-agent LLM) with two label settings (per-comment "Likely AI-generated" badges from a deployed detector, shown or hidden). Coordinated LLM comments did not increase average persuasion over independently generated AI comments, but disproportionately converted readers who initially disagreed and compressed opinion diversity by 12%, manufacturing apparent consensus where genuine disagreement existed. The labels reduced the perceived believability of comments but did not reliably reduce actual opinion change. These findings reveal what we call a Knowing-Resisting dissociation between source recognition and behavioral resistance, and indicate that per-comment disclosure alone is insufficient to counter coordinated LLM opinion campaigns.

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 (XAI-Driven Detection and Attribution of LLM-Generated Comments), 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.