  Rapid information diffusion and large-scaled information cascades can enable
the undesired spread of false information. A small-scaled false information
outbreak may potentially lead to an infodemic. We propose a novel information
diffusion and intervention technique to combat the spread of false news. As
false information is often spreading faster in a social network, the proposed
diffusion methodology inhibits the spread of false news by proactively
diffusing the fact-checked information. Our methodology mainly relies on
defining the potential super-spreaders in a social network based on their
centrality metrics. We run an extensive set of experiments on different
networks to investigate the impact of centrality metrics on the performance of
the proposed diffusion and intervention models. The obtained results
demonstrate that empowering the diffusion of fact-checked news combats the
spread of false news further and deeper in social networks.
