Disrupting Networks: Amplifying Social Dissensus via Opinion Perturbation and Large Language Models

TL;DR

This paper explores how targeted content injection and large language models can strategically disrupt social networks by amplifying social dissensus, with implications for moderation and misinformation campaigns.

Contribution

It introduces a reinforcement learning framework to fine-tune LLMs for maximizing social network disruption, extending the FJ model to enable effective perturbations.

Findings

Tuned LLMs can approach theoretical disruption limits.

Simple FJ variants are ineffective for significant perturbation.

Alteration of individual opinions can maximize network disruption.

Abstract

We study how targeted content injection can strategically disrupt social networks. Using the Friedkin-Johnsen (FJ) model, we utilize a measure of social dissensus and show that (i) simple FJ variants cannot significantly perturb the network, (ii) extending the model enables valid graph structures where disruption at equilibrium exceeds the initial state, and (iii) altering an individual's inherent opinion can maximize disruption. Building on these insights, we design a reinforcement learning framework to fine-tune a Large Language Model (LLM) for generating disruption-oriented text. Experiments on synthetic and real-world data confirm that tuned LLMs can approach theoretical disruption limits. Our findings raise important considerations for content moderation, adversarial information campaigns, and generative model regulation.