Abstract
This research proposes the Uninformed-Spreaders-Debunkers (USD) model, an innovative adaptation of the classical epidemiological Susceptible-Infected-Recovered (SIR) framework, tailored to examine the spread of misinformation within social networks. The USD model distinguishes individual roles into three categories – uninformed (U), spreaders (S), and debunkers (D) – and combines network features with user characteristics to provide a holistic understanding of the mechanics of misinformation dissemination. We argue that the composition of uninformed, spreaders, and debunkers in the network can affect the patterns of misinformation propagation. To test this idea, this study uses a novel agent-based modeling based on real-world datasets to simulate the intricate propagation pathways of misinformation. The USD model addresses a critical research gap by considering both the networked aspects of social media and the multifaceted user interactions within these platforms. Results suggest that misinformation spreads differently across network types and that debunking styles, the initialized ratio of debunkers, and user interactions significantly affect the reach and control of misinformation in a large social network. These findings provide theoretical and practical insights into designing effective strategies to combat misinformation online.