Online social networks have become key platforms for information dissemination, including the facilitation and spread of misinformation, which can contribute to terrorist activities and radicalization. Understanding how network structure influences the spread of false information is essential for developing effective mitigation strategies. In this study, we propose a novel framework that models heterogeneous online contact patterns based on a user’s structural position within a network. Specifically, we estimate a k-core-specific interaction matrix by adapting social contact matrix estimation from infectious disease modelling. We quantify how different structural groups contribute to the propagation of misinformation. We illustrate our approach using empirical data from the Twitter network surrounding the Higgs boson discovery event. Our findings suggest that highly connected users within deeper network cores act as primary amplifiers of misinformation and that susceptibility to misinformation varies systematically across k-core levels. These insights offer valuable parameters for improving misinformation detection and intervention strategies in online environments.