  Online misinformation has been considered as one of the top global risks as
it may cause serious consequences such as economic damages and public panic.
The misinformation prevention problem aims at generating a positive cascade
with appropriate seed nodes in order to compete against the misinformation. In
this paper, we study the misinformation prevention problem under the prominent
independent cascade model. Due to the #P-hardness in computing influence, the
core problem is to design effective sampling methods to estimate the function
value. The main contribution of this paper is a novel sampling method.
Different from the classic reverse sampling technique which treats all nodes
equally and samples the node uniformly, the proposed method proceeds with a
hybrid sampling process which is able to attach high weights to the users who
are prone to be affected by the misinformation. Consequently, the new sampling
method is more powerful in generating effective samples used for computing seed
nodes for the positive cascade. Based on the new hybrid sample technique, we
design an algorithm offering a $(1-1/e-\epsilon)$-approximation. We
experimentally evaluate the proposed method on extensive datasets and show that
it significantly outperforms the state-of-the-art solutions.
