  The rapid and widespread dissemination of misinformation through social
networks is a growing concern in today's digital age. This study focused on
modeling fake news diffusion, discovering the spreading dynamics, and designing
control strategies. A common approach for modeling the misinformation dynamics
is 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 on
neighbors' beliefs, hoax credibility, spreading rate, probability of verifying
the news, and probability of forgetting the current state. Our contribution is
to push this model to real social networks by considering different classes of
agents with their characteristics. We proposed two main strategies for
confronting misinformation diffusion. First, we can educate a minor class, like
scholars or influencers, to improve their ability to verify the news or
remember their state longer. The second strategy is adding fact-checker bots to
the network to spread the facts and influence their neighbors' states. Our
result shows that both of these approaches can effectively control the
misinformation spread.
