  Online social networks are used to diffuse opinions and ideas among users,
enabling a faster communication and a wider audience. The way in which opinions
are conditioned by social interactions is usually called social influence.
Social influence is extensively used during political campaigns to advertise
and support candidates. Herein we consider the problem of exploiting social
influence in a network of voters in order to change their opinion about a
target candidate with the aim of increasing his chance to win/lose the election
in a wide range of voting systems.
  We introduce the Linear Threshold Ranking, a natural and powerful extension
of the well-established Linear Threshold Model, which describes the change of
opinions taking into account the amount of exercised influence. We are able to
maximize the score of a target candidate up to a factor of $1-1/e$ by showing
submodularity. We exploit such property to provide a
$\frac{1}{3}(1-1/e)$-approximation algorithm for the constructive election
control problem. Similarly, we get a $\frac{1}{2}(1-1/e)$-approximation ratio
in the destructive scenario. The algorithm can be used in arbitrary scoring
rule voting systems, including plurality rule and borda count. Finally, we
perform an experimental study on real-world networks, measuring Probability of
Victory (PoV) and Margin of Victory (MoV) of the target candidate, to validate
the model and to test the capability of the algorithm.
