A Computational Study of Misinformation Diffusion and Public Opinion Formation in Online News Networks (Journal of Advanced Artificial Intelligence Research, 2026)
The proliferation of misinformation within online news networks presents a critical challenge to democratic discourse and public trust. This paper presents a computational investigation into the mechanisms by which false or misleading information spreads across digital platforms and shapes collective opinion. We develop an agent-based modeling framework that integrates network topology, cognitive biases, and platform-driven content curation algorithms to simulate the coupled dynamics of information diffusion and opinion formation. The study emphasizes system-level architectural choices and their structural trade-offs, examining how different governance strategies—including content moderation, algorithmic transparency, and user empowerment—affect the robustness and fairness of information ecosystems. Our simulations reveal that homophilic network structures and reinforcement learning in recommender systems amplify echo chambers and accelerate the spread of low-veracity content, while diversity-inducing algorithms can increase resilience but may incur costs in user engagement and personalization accuracy. We further analyze the sustainability of intervention policies under adversarial manipulation and assess the implications for socio-technical infrastructure design. The findings underscore the need for multi-stakeholder governance approaches that balance freedom of expression with collective epistemic welfare, and highlight critical directions for future research in computational social science, human-computer interaction, and public policy.
Keywords: misinformation diffusion, public opinion formation, online news networks, computational modeling, algorithmic governance, robustness, fairness, socio-technical infrastructure