Research projects

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 (1) the analysis of digital data to identify themes and discursive frames surrounding conspiracy beliefs across different social media platforms in Italy; (2) a national survey to collect population-representative data on (offline and online) personal networks, conspiracy beliefs, and health; (3) 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.

  • Team: Raffaele Vacca (PI), Viviana Amati (co-PI, University of Milan-Bicocca), Massimo Airoldi (University of Milan), Federico Bianchi (University of Milan)
  • Funding agency: Italian Ministry of University and Research (PRIN P2022955FC)

Implications of shifting migration trends: social networks, health, and work of migrant farm workers in Florida

This project is based on personal network survey data collected among Mexican and Haitian farm workers in different Florida rural locations. The research examines personal networks, social support, and physical and mental health outcomes in Florida’s new communities of migrant farm workers. We also consider work conditions, legal status and racial discrimination as potential social determinants of health.

Measuring, modeling, and tracking the contribution of academic research to the UN Sustainable Development Goals

The UN Sustainable Development Goals (SDGs) are 17 global development goals for 2030 agreed upon by all United Nations Member States. With their 169 targets, they provide a blueprint and plan of action for development, prosperity and peace for people and the planet, including strategies to end poverty and deprivation, fuel economic growth, tackle climate change, improve health and education, and reduce inequality globally. This research project aims to (1) develop a method to measure the extent to which academic research aligns with and contributes to the SDGs and their targets; and (2) pilot a prototype tracking tool to measure and monitor SDG relevance of research in universities.

Past projects

The “migrant Roma”: from exclusionary processes to resources for social integration

This project studies migration and incorporation trajectories of Roma migrants in French metropolitan areas, with a focus on personal networks and social support. It is based on one of the first quantitative surveys of personal networks in the Roma migrant community in France, a particularly hard-to-reach population. The project aims to identify and describe sources of migration capabilities and factors that shape the exchange of informal and institutional support to Roma migrants in French cities. This research is funded in part by Sciences Po Paris and conducted in collaboration with Tommaso Vitale (PI, Sciences Po Center for European Studies).

The emergence of Covid-19 team science: tracking topics, networks and expertise in global Covid-19 research

The Covid-19 pandemic is having a transformative impact on science, accelerating the convergence of a highly interdisciplinary and dynamic “team science” field of coronavirus/Covid-19 research. This project analyzes a unique and constantly updated combination of “big” bibliographic data to track evolving topics, growing networks, and hidden expertise in global and local coronavirus/Covid-19 research. We use CORD-19, a growing dataset of over 63,000 coronavirus/Covid-19 scientific articles, and Dimensions, a global database of approximately 100 million publications, grants, and patents with detailed author information. We draw on theories and methods from computational social science, network science and computational linguistics to examine the growth, diversification and evolution of topics and networks in global Covid-19 research over the past decades, years, and months. Funded by the UF Informatics Institute, this project is a collaboration between Raffaele Vacca (PI), Kevin Tang (co-PI, UF Linguistics), Ilaria Capua (UF One Health), and Till Krenz and Christopher McCarty (UF BEBR).

UF CTSI Network Science Program

This program of the UF Clinical and Translational Science Institute coordinates different projects that apply network science and computational methods to the study of team science and scientific collaboration. Topics of interest include the formation of research communities and interdisciplinary collaborations within universities; the relationship between topic similarity and co-authorship between scientists; individual and institutional factors shaping cross-university collaborations; prestige and inequality in academic science; and modeling the emergence and decline of scientific ideas. Supported in part by the NIH National Center for Advancing Translational Sciences (award number UL1TR001427), these projects are conducted by the BEBR/CTSI Network Science Lab, co-led by Christopher McCarty and Raffaele Vacca.