ConsNet: Conspiracy beliefs, personal networks, and health in the post-pandemic era

Conspiracy beliefs are one of the most salient and consequential phenomena of our times, with important impacts on individual and social life in a wide range of domains, from politics to culture and health. The COVID-19 pandemic and the growth of online social media have fueled an unprecedented diffusion of conspiracy beliefs across the globe, some exacerbating public health threats such as anti-vaccine misinformation and movements. This project proposes a novel, sociological approach to study (1) the characteristics of conspiracy discourses on social media after the pandemic; (2) the social determinants of conspiracy beliefs, online and offline; and (3) the impacts these beliefs have on individual and population health in Italy.

We focus on social isolation and social network homogeneity as two major social determinants of conspiracy beliefs, and examine the consequences of conspiracy thinking on health beliefs, behaviors, and outcomes for individuals and groups. Considering Italy’s ongoing and historical transition to high levels of immigration and ethnic diversity, we also investigate potential differences in antecedents and consequences of conspiracy beliefs between migrants and the non-migrant ethnic majority.

In a sequential mixed-methods design, the project collects quantitative and qualitative data, both online and offline, among migrants and non-migrants in Italy. Research steps include:

• The analysis of digital data to identify themes and discursive frames surrounding conspiracy beliefs across different social media platforms in Italy;
• A national survey to collect population-representative data on (offline and online) personal networks, conspiracy beliefs, and health
• In-depth interviews to elucidate mechanisms of association between the variables of interest.

Data analysis relies on various methods, including statistical modeling of survey and social network data; computational methods such as natural language processing and network science techniques; traditional content analysis of interview and digital data; and empirically grounded agent-based modeling.