Faculty of Science, Technology and Medicine
Faculty of Law, Economics and Finance
Faculty of Humanities, Education and Social Sciences
Interdisciplinary Centre for Security, Reliability and Trust
Luxembourg Centre for Systems Biomedicine
Luxembourg Centre for Contemporary and Digital History
Luxembourg Centre for European Law
Luxembourg Centre for Socio-Environmental Systems
The Doctoral School in Science and Engineering is happy to invite you to Yuwei CHUAI’s defence entitled
Computational Analysis of Misinformation Engagement and Interventions on Social Media
Supervisor: Assoc. Prof Gabriele LENZINI
The widespread use of social media has transformed how information is created, shared, and consumed, but it has also enabled the rapid diffusion of misinformation with serious consequences for public health, democratic processes, and social trust. Despite extensive research on misinformation, important gaps remain in understanding how users engage with misleading content during health crises, how fact-checking organizations select claims for verification, and how effective emerging fact-checking interventions are in real-world settings. This thesis addresses these gaps by examining misinformation diffusion and fact-checking practices on social media, with a particular focus on the role and effectiveness of crowdsourced fact-checking approach, i.e., the Community Notes system deployed on X (formerly Twitter).
First, the thesis advances the understanding of user engagement with misinformation during health crises. Using the COVID-19 pandemic as a case study, it shows that misinformation often spans multiple topics and incorporates conspiracy narratives, which increases its attractiveness to users. Crucially, the thesis distinguishes between different user types on social media — those who share external news items and those who react to them on X — and demonstrates that these groups exhibit different engagement patterns. While news sharers contribute to the persistence of misinformation over time, post viewers are more likely to engage with content characterized by topic diversity and conspiratorial framing.