Layered Network Diffusion of Misinformation on YouTube: A Multi-Level Analysis of Video and Channel Interactions

Misinformation has become a persistent feature of contemporary digital information environments. Platform designs and business models often privilege attention, engagement, and repeated exposure over epistemic quality. However, misinformation does not diffuse uniformly across platform structures. This study examines how contested claims in a South Korean social policy controversy circulate on YouTube. The analysis focuses on unfounded allegations regarding permanent employment offers to part-time workers at Incheon International Airport across two analytic levels: (1) a videoclip network, in which video-to-video ties are formed through shared commenters over time, and (2) a channel network, in which channel-to-channel ties are formed through shared commenters over time. Drawing on YouTube Data API records, we employ a mixed computational approach that integrates social network analysis, speech-to-text transcription, natural language processing, semantic network analysis, and automated content classification. Videos are classified as misinformation or non-misinformation based on the presence of demonstrably incorrect claims or corrective content. We compare network structure, diffusion patterns, and engagement dynamics across these two layers. The results reveal pronounced layer-specific differences. Misinformation diffuses more extensively within the channel network, which exhibits higher density and stronger cross-channel interconnectedness, suggesting that creator-level infrastructures function as stabilising conduits for the circulation of false claims. By contrast, diffusion pathways at the videoclip level show comparatively weaker differentiation between misinformation and non-misinformation content. Engagement patterns also diverge misinformation videos attract significantly more likes, while message format and channel attributes are less consistently distinguishing. From a theoretical standpoint, this study advances a multi-layer diffusion perspective on platform-mediated misinformation by demonstrating how platform architectures shape the visibility, persistence, and amplification of false claims. The findings highlight the importance of intervention strategies that move beyond individual content moderation toward creator- and network-level governance mechanisms, with implications for the design of platform features, recommendation systems, and misinformation mitigation tools.