  The proliferation of harmful content and misinformation on social networks
necessitates content moderation policies to maintain platform health. One such
policy is shadow banning, which limits content visibility. The danger of shadow
banning is that it can be misused by social media platforms to manipulate
opinions. Here we present an optimization based approach to shadow banning that
can shape opinions into a desired distribution and scale to large networks.
Simulations on real network topologies show that our shadow banning policies
can shift opinions and increase or decrease opinion polarization. We find that
if one shadow bans with the aim of shifting opinions in a certain direction,
the resulting shadow banning policy can appear neutral. This shows the
potential for social media platforms to misuse shadow banning without being
detected. Our results demonstrate the power and danger of shadow banning for
opinion manipulation in social networks.
