Rumor spreading is a ubiquitous phenomenon in social and technological
networks. Traditional models consider that the rumor is propagated by pairwise
interactions between spreaders and ignorants. Spreaders can become stiflers
only after contacting spreaders or stiflers. Here we propose a model that
considers the traditional assumptions, but stiflers are active and try to
scotch the rumor to the spreaders. An analytical treatment based on the theory
of convergence of density dependent Markov chains is developed to analyze how
the final proportion of ignorants behaves asymptotically in a finite
homogeneously mixing population. We perform Monte Carlo simulations in random
graphs and scale-free networks and verify that the results obtained for
homogeneously mixing populations can be approximated for random graphs, but are
not suitable for scale-free networks. Furthermore, regarding the process on a
heterogeneous mixing population, we obtain a set of differential equations that
describes the time evolution of the probability that an individual is in each
state. Our model can be applied to study systems in which informed agents try
to stop the rumor propagation. In addition, our results can be considered to
develop optimal information dissemination strategies and approaches to control
rumor propagation.Comment: 13 pages, 11 figure
