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Recent social networks' misinformation mitigation approaches tend toinvestigate how to reduce misinformation by considering a whole-networkstatistical scale. However, unbalanced misinformation exposures amongindividuals urge to study fair allocation of mitigation resources. Moreover,the network has random dynamics which change over time. Therefore, we introducea stochastic and non-stationary knapsack problem, and we apply its resolutionto mitigate misinformation in social network campaigns. We further propose ageneric misinformation mitigation algorithm that is robust to different socialnetworks' misinformation statistics, allowing a promising impact in real-worldscenarios. A novel loss function ensures fair mitigation among users. Weachieve fairness by intelligently allocating a mitigation incentivizationbudget to the knapsack, and optimizing the loss function. To this end, a teamof Learning Automata (LA) drives the budget allocation. Each LA is associatedwith a user and learns to minimize its exposure to misinformation by performinga non-stationary and stochastic walk over its state space. Our results show howour LA-based method is robust and outperforms similar misinformation mitigationmethods in how the mitigation is fairly influencing the network users.

Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization