  Recently, online social networks have become major battlegrounds for
political campaigns, viral marketing, and the dissemination of news. As a
consequence, ''bad actors'' are increasingly exploiting these platforms,
becoming a key challenge for their administrators, businesses and the society
in general. The spread of fake news is a classical example of the abuse of
social networks by these actors. While some have advocated for stricter
policies to control the spread of misinformation in social networks, this often
happens in detriment of their democratic and organic structure. In this paper
we study how to limit the influence of a target set of users in a network via
the removal of a few edges. The idea is to control the diffusion processes
while minimizing the amount of disturbance in the network structure.
  We formulate the influence limitation problem in a data-driven fashion, by
taking into account past propagation traces. Moreover, we consider two types of
constraints over the set of edge removals, a budget constraint and also a, more
general, set of matroid constraints. These problems lead to interesting
challenges in terms of algorithm design. For instance, we are able to show that
influence limitation is APX-hard and propose deterministic and probabilistic
approximation algorithms for the budgeted and matroid version of the problem,
respectively. Our experiments show that the proposed solutions outperform the
baselines by up to 40%.
