Network disruption: maximizing disagreement and polarization in social networks

TL;DR

This paper models how an adversary can take over limited social media profiles to maximize disagreement and polarization, revealing vulnerabilities in social networks and demonstrating effective disruption strategies.

Contribution

It introduces a simple yet effective model of network disruption, providing theoretical bounds and empirical analysis on how adversaries can increase polarization.

Findings

Adversaries always push taken-over profiles to extreme opinions.

Disagreement and polarization increase linearly with the number of compromised profiles.

Simple heuristics like targeting centrists can significantly disrupt networks.

Abstract

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.