Uncertain rumor refutation optimization using a multi-strategy whale optimization algorithm

Media technology’s development has led to more diversified forms of online rumors, whose rampant spread seriously threatens the information ecosystem. This paper delves into the problem of governing the propagation of online rumors under uncertain circumstances. Firstly, by taking into account the impacts of multiple constraints, an uncertain optimistic value-variance-entropy bi-objective rumor refutation optimization model is constructed, where optimistic value quantifies refutation effect, variance evaluates risk, and entropy assesses platform diversity. Second, the crisp equivalent forms of the model under different distributions are explored. Furthermore, a multi-strategy multi-objective whale algorithm combining logistic mapping, inertia weight and Lévy flight strategy is proposed to solve the above bi-objective model. Finally, numerical simulations are carried out to verify the practicability of the presented model. The performance of different online rumor-refuting platforms is compared, and the proposed algorithm is analyzed against other algorithms. The results indicate that when the government takes the lead and cooperates with other online rumor-refuting platforms, the effect of rumor refutation can be maximized while the risk is minimized.