All the ties that bind. A socio-semantic network analysis of Twitter political discussions

Social media play a crucial role in what contemporary sociological reflections define as a ‘hybrid media system’. Online spaces created by social media platforms resemble global public squares hosting large-scale social networks populated by citizens, political leaders, parties and organizations, journalists, activists and institutions that establish direct interactions and exchange contents in a disintermediated fashion. In the last decade, an increasing number of studies from researchers coming from different disciplines has approached the study of the manifold facets of citizen participation in online political spaces. In most cases, these studies have focused on the investigation of direct relationships amongst political actors. Conversely, relatively less attention has been paid to the study of contents that circulate during online discussions and how their diffusion contributes to building political identities. Even more rarely, the study of social media contents has been investigated in connection with those concerning social interactions amongst online users. To fill in this gap, my thesis work proposes a methodological procedure consisting in a network-based, data-driven approach to both infer communities of users with a similar communication behavior and to extract the most prominent contents discussed within those communities. More specifically, my work focuses on Twitter, a social media platform that is widely used during political debates. Groups of users with a similar retweeting behavior - hereby referred to as discursive communities - are identified starting with the bipartite network of Twitter verified users retweeted by nonverified users. Once the discursive communities are obtained, the corresponding semantic networks are identified by considering the co-occurrences of the hashtags that are present in the tweets sent by their members. The identification of discursive communities and the study of the related semantic networks represent the starting point for exploring more in detail two specific conversations that took place in the Italian Twittersphere: the former occured during the electoral campaign before the 2018 Italian general elections and in the two weeks after Election day; the latter centered on the issue of migration during the period May-November 2019. Regarding the social analysis, the main result of my work is the identification of a behavior-driven picture of discursive communities induced by the retweeting activity of Twitter users, rather than determined by prior information on their political affiliation. Although these communities do not necessarily match the political orientation of their users, they are closely related to the evolution of the Italian political arena. As for the semantic analysis, this work sheds light on the symbolic dimension of partisan dynamics. Different discursive communities are, in fact, characterized by a peculiar conversational dynamics at both the daily and the monthly time-scale. From a purely methodological aspect, semantic networks have been analyzed by employing three (increasingly restrictive) benchmarks. The k-shell decomposition of both filtered and non-filtered semantic networks reveals the presence of a core-periphery structure providing information on the most debated topics within each discursive community and characterizing the communication strategy of the corresponding political coalition.