Research
How and why people trust health information — and how social identities, networks, and cultural contexts shape that trust.
My 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 spans the spread of health misinformation, prebunking interventions, and the role of artificial intelligence in the information ecosystem.
Social networks and belief formation
A central thread of my work examines how social networks shape political and health beliefs. Early research found that network homogeneity predicts political polarization and susceptibility to misinformation — people embedded in more homogeneous networks tend to hold more extreme views and encounter less corrective information. Building on this, my colleagues and I showed that political network composition predicts vaccination attitudes, highlighting how social environments shape health beliefs.
Trust in science and health institutions
I study trust in scientists and scientific institutions across social, cultural, and religious contexts. I'm a co-author on a 68-country study in Nature Human Behaviour assessing public trust in scientists, drawing on roughly 69,000 respondents. This work informs my collaboration with the Edelman Trust Institute at Yale on longitudinal trends in healthcare trust, and the Georgetown-Lancet Commission on Faith and Health, where I analyze how religious communities engage with health information online.
Media literacy and prebunking interventions
I design and evaluate media literacy interventions that build resistance to misinformation before exposure. I helped develop and test Gali Fakta, a prebunking game for Indonesian audiences that significantly reduced belief in false information and the likelihood of sharing it. My cross-cultural research compares prebunking games across the United States and Indonesia.
Artificial intelligence and the information ecosystem
My recent work explores the role of AI in the information and social media ecosystem — including how citations influence trust in large language model responses, and AI-augmented approaches to community fact-checking for health misinformation.
- Cologna, V., Mede, N. G., … Facciani, M. (2025). Trust in scientists and their role in society across 68 countries. Nature Human Behaviour, 9(4), 713–730. Link →
- Facciani, M. (2025). Misguided: Where Misinformation Starts, How It Spreads, and What to Do About It. Columbia University Press. Link →
- Facciani, M., Huang, Q., & Weninger, T. (2026). Cross-cultural media literacy interventions: comparing Gali Fakta and Harmony Square in Indonesia and the United States. Humanities and Social Sciences Communications, 13: 288. Link →
- Facciani, M., Lazić, A., Viggiano, G., & McKay, T. (2023). Political network composition predicts vaccination attitudes. Social Science & Medicine, 328: 116004. Link →
- Facciani, M. J., Apriliawati, D., & Weninger, T. Playing Gali Fakta inoculates Indonesian participants against false information. Harvard Kennedy School Misinformation Review. Link →
- Ding, Y., Facciani, M., et al. Citations and Trust in LLM Generated Responses. Proceedings of the AAAI Conference on Artificial Intelligence, 39. Link →
For the complete list of publications and citations, see my Google Scholar profile.
I have taught courses at the undergraduate and graduate level on misinformation and polarization (Vanderbilt University, syllabus), the psychology of belief (Antioch University), and psychological statistics (University of South Carolina, five semesters). I completed the Preparing Future Faculty Program and frequently give guest lectures on misinformation, media literacy, and science communication.
I also offer online courses through Substack, making this material accessible to learners outside academia.