  Due to the extensive role of social networks in social media, it is easy for
people to share the news, and it spreads faster than ever before. These
platforms also have been exploited to share the rumor or fake information,
which is a threat to society. One method to reduce the impact of fake
information is making people aware of the correct information based on hard
proof. In this work, first, we propose a propagation model called Competitive
Independent Cascade Model with users' Bias (CICMB) that considers the presence
of strong user bias towards different opinions, believes, or political parties.
We further propose a method, called $k-TruthScore$, to identify an optimal set
of truth campaigners from a given set of prospective truth campaigners to
minimize the influence of rumor spreaders on the network. We compare
$k-TruthScore$ with state of the art methods, and we measure their performances
as the percentage of the saved nodes (nodes that would have believed in the
fake news in the absence of the truth campaigners). We present these results on
a few real-world networks, and the results show that $k-TruthScore$ method
outperforms baseline methods.
