The most extreme positions against COVID-19 containment measures are concentrated in democratic countries. In such countries, freedom of speech is guaranteed, being the essence of modern democracy, but there are concerns about the risks that this freedom implies for the entire community. We are facing what some scholars call “the dilemma” of democracies. Adding to the severity of this dilemma is the significant communicative power of social networks. In this study, we analyze the debate on the green pass in Italy by collecting Twitter data from September 25, 2021 to October 22, 2021, a period marked by numerous social protests against anti-pandemic measures. We then construct a two-mode Semantic Network, which is a bipartite graph that describes connections between two types of nodes: social actors (i.e., Twitter users) and semantic concepts (derived from the users’ tweets). We analyze this network to detect communities of users and concepts using a specific two-mode community detection approach. The objective is to identify communities of users expressing different and possibly polarized opinions and concepts within the green pass debate. Subsequently, a Textual Correspondence Analysis will reveal differences in lexicon usage among tweets belonging to different communities. The results demonstrate that the combination of these two techniques can shed light on the differences or similarities among groups of users posting about a particular trending topic, particularly regarding the radical positions aligned with the COVID-19 pandemic “deniers”.
