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The rapid and widespread dissemination of misinformation through socialnetworks is a growing concern in today's digital age. This study focused onmodeling fake news diffusion, discovering the spreading dynamics, and designingcontrol strategies. A common approach for modeling the misinformation dynamicsis SIR-based models. Our approach is an extension of a model called 'SBFC'which is a SIR-based model. This model has three states, Susceptible, Believer,and Fact-Checker. The dynamics and transition between states are based onneighbors' beliefs, hoax credibility, spreading rate, probability of verifyingthe news, and probability of forgetting the current state. Our contribution isto push this model to real social networks by considering different classes ofagents with their characteristics. We proposed two main strategies forconfronting misinformation diffusion. First, we can educate a minor class, likescholars or influencers, to improve their ability to verify the news orremember their state longer. The second strategy is adding fact-checker bots tothe network to spread the facts and influence their neighbors' states. Ourresult shows that both of these approaches can effectively control themisinformation spread.

Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents