Destabilizing a Social Network Model via Intrinsic Feedback Vulnerabilities

TL;DR

This paper demonstrates how small, targeted perturbations in a social network model can destabilize the system and potentially lead to widespread radicalization, highlighting vulnerabilities in social influence dynamics.

Contribution

It introduces a novel application of robust control theory to identify minimal, destabilizing perturbations in social influence models, revealing potential risks in social network stability.

Findings

Small perturbations can cause radicalization of all agents.

Targeted network modifications can destabilize social influence systems.

Method applicable to real social networks for vulnerability assessment.

Abstract

Social influence plays a significant role in shaping individual sentiments and actions, particularly in a world of ubiquitous digital interconnection. The rapid development of generative AI has engendered well-founded concerns regarding the potential scalable implementation of radicalization techniques in social media. Motivated by these developments, we present a case study investigating the effects of small but intentional perturbations on a simple social network. We employ Taylor's classic model of social influence and tools from robust control theory (most notably the Dynamical Structure Function (DSF)), to identify perturbations that qualitatively alter the system's behavior while remaining as unobtrusive as possible. We examine two such scenarios: perturbations to an existing link and perturbations that introduce a new link to the network. In each case, we identify destabilizing perturbations of minimal norm and simulate their effects. Remarkably, we find that small but targeted alterations to network structure may lead to the radicalization of all agents, exhibiting the potential for large-scale shifts in collective behavior to be triggered by comparatively minuscule adjustments in social influence. Given that this method of identifying perturbations that are innocuous yet destabilizing applies to any suitable dynamical system, our findings emphasize a need for similar analyses to be carried out on real systems (e.g., real social networks), to identify the places where such dynamics may already exist.