  We study a model for the spread of fake news, where first a piece of fake
news is spread from a location in a network, followed by a correction to the
news. We assume that both the fake as well as correct news travel as
first-passage percolations or SI epidemics with i.i.d.\ traversal times,
possibly with different distributions and dependence.
  We make the (hopeful) assumption that once a vertex in the network hears the
correct news, they are immediately convinced that this is indeed correct, and
continue to spread the correct news. Even in this optimistic scenario, our main
results show that it is very difficult to remove the fake news from the
network, even when the correct news would spread much faster than the fake
news. We show this on the configuration model, a model that has gained enormous
popularity as a simple, yet flexible, model for real-world networks.
  The crux of the proof is the realization that this problem on a branching
process tree (which is known to be the local limit of the configuration model)
can be reformulated in terms of the maximum of a branching random walk, a topic
that has attracted considerable attention in the past decade. We then extend
the results to the graph setting using couplings to branching processes, local
convergence and detailed estimates on first-passage percolation on random
graphs as derived in \cite{BhaHofHoo17}. Remarkably, despite the fact that our
proofs for the configuration model rely on its local branching process
structure, the condition for strong survival on a finite number of generations
of a branching process tree is {\em different} from that on the configuration
model.
