Several elections run in the last years have been characterized by attempts
to manipulate the result of the election through the diffusion of fake or
malicious news over social networks. This problem has been recognized as a
critical issue for the robustness of our democracy. Analyzing and understanding
how such manipulations may occur is crucial to the design of effective
countermeasures to these practices.
Many studies have observed that, in general, to design an optimal
manipulation is usually a computationally hard task. Nevertheless, literature
on bribery in voting and election manipulation has frequently observed that
most hardness results melt down when one focuses on the setting of (nearly)
single-peaked agents, i.e., when each voter has a preferred candidate (usually,
the one closer to her own belief) and preferences of remaining candidates are
inversely proportional to the distance between the candidate position and the
voter's belief. Unfortunately, no such analysis has been done for election
manipulations run in social networks.
In this work, we try to close this gap: specifically, we consider a setting
for election manipulation that naturally raises (nearly) single-peaked
preferences, and we evaluate the complexity of election manipulation problem in
this setting: while most of the hardness and approximation results still hold,
we will show that single-peaked preferences allow to design simple, efficient
and effective heuristics for election manipulation