The ‘words’ of no green pass communities on twitter: a two-mode semantic network analysis

The most extreme positions against anti pandemic government measures are concentrated in the most democratic countries. Therefore, on the one hand, freedom of speech is guaranteed, being it the essence of modern democracy; on the other stands the awareness of the risks that this freedom implies for the entire community. We are facing what some scholars call "the dilemma" of democracies. Increasing the drastic consequences of this dilemma is undoubtedly the communicative power of social networks. In this work, starting from data collected on Twitter from 25-Sep-2021 to 22-Oct-2021 (that is the period when local elections in many important cities of Italy took place) concerning the green pass debate in Italy, we construct a two-mode Semantic Network, which is a bipartite graph that describes connections between two types of nodes, social actors (in our case twitter users) and semantic concepts. In particular, in the first step data-tweets size will be significantly reduced; in the second step, a content analysis will be run to pinpoint the concepts underneath them. To detect communities of users and concepts, we use a proper two-mode community detection approach, i.e. an extension of the fastgreedy suited for the bipartite network called “DIRTLPAwb+”. The aim is to identify communities of users expressing different opinions and concepts within the green pass debate.