  The spread of rumors through social media and online social networks can not
only disrupt the daily lives of citizens but also result in loss of life and
property. A rumor spreads when individuals, who are unable decide the
authenticity of the information, mistake the rumor as genuine information and
pass it on to their acquaintances. We propose a solution where a set of
individuals (based on their degree) in the social network are trained and
provided resources to help them distinguish a rumor from genuine information.
By formulating an optimization problem we calculate the optimum set of
individuals, who must undergo training, and the quality of training that
minimizes the expected training cost and ensures an upper bound on the size of
the rumor outbreak. Our primary contribution is that although the optimization
problem turns out to be non convex, we show that the problem is equivalent to
solving a set of linear programs. This result also allows us to solve the
problem of minimizing the size of rumor outbreak for a given cost budget. The
optimum solution displays an interesting pattern which can be implemented as a
heuristic. These results can prove to be very useful for social planners and
law enforcement agencies for preventing dangerous rumors and misinformation
epidemics.
