Shaping Opinions in Social Networks with Shadow Banning

TL;DR

This paper presents an optimization-based approach to shadow banning in social networks, capable of shaping opinions and polarization, revealing both its potential and risks for manipulation and neutrality concealment.

Contribution

It introduces a scalable optimization method for shadow banning that can manipulate opinions in social networks while appearing neutral, highlighting its misuse potential.

Findings

Shadow banning can effectively shift opinions and polarization.

Shadow banning policies can appear neutral despite manipulation.

The approach scales to large social network topologies.

Abstract

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.