In recent years, disinformation circulating the internet and especially social media has become a widespread concern. The urgency of the fake news problem lies in the fact that decisions that are taken on false or misleading information risk impacting democratic processes negatively. This is especially true during a global health crisis when the misinformation in question concerns scientific facts and informs the way people act in society. Focusing on the relational aspect of fake news, new insight and hypothesis generation can be explored with a relatively novel method, social network analysis. This research provides with an example of the method applied to political problems by analysing the misinformation and fact-checking diffusion network on the Italian Twitterverse during the second wave of COVID19. The network shows a tight core of misinformation and a peripheral fact-checking region approximating a spanning tree. Although some levels of polarization are observed, the resulting network shows no evidence of echo chambers that hinder interaction between the misinformation and the fact-checking clusters. Actor-level analysis revealed that the majority of the users interacting in the network are humans and that influential and active users share misinformation only. The findings of this work are presented to show how network analysis can contribute both mitigation strategies in particular and to social and political sciences research in general.