Matthew Facciani

Matthew Facciani is a social scientist who studies how and why people trust health information, and how social identities, networks, and cultural contexts shape that trust. His research examines how information environments influence belief formation, what drives skepticism toward science and health institutions, and what interventions can strengthen public engagement with evidence. This work includes research on the spread of health misinformation, prebunking interventions, and the role of artificial intelligence in the information ecosystem.

Social Networks and Belief Formation

A central focus of Facciani’s research examines how people’s social networks shape political and health beliefs. His early work found that network homogeneity predicts political polarization and susceptibility to misinformation, showing that people embedded in more socially and politically homogeneous networks are more likely to hold extreme views and less likely to encounter corrective information. This research applied social identity complexity theory to real-world network data, contributing to scholarship on how echo chambers form not only online, but in everyday social life.

Building on this work, Facciani and colleagues demonstrated that political network composition predicts vaccination attitudes in research published in Social Science & Medicine, highlighting how social environments shape health beliefs. Additional research examined network disruption following politically charged events, including social network loss among LGBTQ+ adults after the 2016 presidential election.

Trust in Science and Health Institutions

Facciani’s research examines trust in scientists and scientific institutions, particularly in the context of health communication, and how trust is shaped across social, cultural, and religious contexts. He is a co-author on a 68-country study published in Nature Human Behaviour that assessed public trust in scientists and their role in society using data from approximately 69,000 respondents. The study identified key predictors of trust, including political orientation, religiosity, and media consumption, and found that social media use was consistently associated with lower levels of trust in scientists across countries.

This work informs his collaboration with the Edelman Trust Institute at Yale, where he contributes to research on longitudinal trends in trust in healthcare and how identity shapes public relationships with health institutions.

Facciani also worked on the Georgetown-Lancet Commission on Faith and Health, analyzing how religious communities engage with health information online. This research examines church social media content and Christian creator networks in the United States and Latin America, focusing on how faith-based messaging shapes vaccine attitudes and broader health beliefs, including culturally specific framings that can complicate public health communication.

Media Literacy and Prebunking Interventions

Facciani studies media literacy interventions designed to build resistance to misinformation before exposure. He helped develop and evaluate Gali Fakta, a “prebunking” game created for Indonesian audiences. His team found that playing Gali Fakta significantly reduces belief in false information and decreases the likelihood of sharing it, with results published in the Harvard Kennedy School Misinformation Review. He has also conducted cross-cultural research on prebunking games in both the United States and Indonesia, showing that these interventions can improve misinformation detection across different cultural contexts. His additional work examines how feedback and educational interventions influence people’s ability to identify manipulated images.

Artificial Intelligence and The Information Ecosystem

Facciani’s recent research explores the role of artificial intelligence in the information and social media ecosystem. He is a co-author on research examining how citations influence trust in large language model responses, with implications for designing AI systems that better support informed decision-making. He is also developing research on AI-augmented approaches to community fact-checking for health misinformation.