An Adversarial Model of Network Disruption: Maximizing Disagreement and Polarization in Social Networks

• Published in: IEEE transactions on network science and engineering Vol. 9; no. 2; pp. 728 - 739
• Main Authors: Chen, Mayee F, Racz, Miklos Z
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blogs; Computer hacking; Digital media; Disruption; Fake news; False information; Force; Maximization; misinformation; Natural algorithms; Optimization; Polarization; Social networking (online); Social networks; Upper bound; User profiles; Weight measurement
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2021.3131416

The spread of misinformation has increased markedly in recent years, 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, 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 to capture this phenomenon, 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 severity of the problem.