The global health crisis arising from the expansion of Covid-19 has led the WHO to coin the term infodemics to define a situation of fear and insecurity in which the dissemination of false information has become widespread. These hoaxes take advantage of this type of emotion to spread faster than the coronavirus itself, generating fear and distrust in the population. The spread of these lies, part of which circulates on social networks, is dangerous because it affects health and can make the contagion worse and cause people to die. This research aims to analyse and visualise the network created around the false news circulating on Twitter about the coronavirus pandemic using the technique of social network analysis. NodeXL Pro software has been used. Several measures of network centrality have been used to generate the network of connections between users, to represent their interaction patterns and to identify the key actors within the network. In addition, a semantic network has also been created to discover the differences in the way groups of people talk about the topic. The results show that the situation in the USA dominates the conversation, despite the fact that at that time there were hardly any cases, and Europe had become the global epicentre of the Covid-19. Despite reports of inaction by journalists and critics of the Trump government, there are several weeks in which disinformation distracts from taking more effective action and actually preventing contagion. Moreover, among the actors with the most prominent positions in the network, there is little presence of scientists and institutions that help to disprove the hoaxes and explain the hygiene measures.