  Recent years have seen a marked increase in the spread of misinformation, a
phenomenon which has been accelerated and amplified by social media such as
Facebook and Twitter. While some actors spread misinformation to push a
specific agenda, it has also been widely documented that others aim to simply
disrupt the network by increasing disagreement and polarization across the
network and thereby destabilizing society. Popular social networks are also
vulnerable to large-scale attacks. Motivated by this reality, we introduce a
simple model of network disruption where an adversary can take over a limited
number of user profiles in a social network with the aim of maximizing
disagreement and/or polarization in the network.
  We investigate this model both theoretically and empirically. We show that
the adversary will always change the opinion of a taken-over profile to an
extreme in order to maximize disruption. We also prove that an adversary can
increase disagreement / polarization at most linearly in the number of user
profiles it takes over. Furthermore, we present a detailed empirical study of
several natural algorithms for the adversary on both synthetic networks and
real world (Reddit and Twitter) data sets. These show that even simple,
unsophisticated heuristics, such as targeting centrists, can disrupt a network
effectively, causing a large increase in disagreement / polarization. Studying
the problem of network disruption through the lens of an adversary thus
highlights the seriousness of the problem.
