A Study of Three Influencer Archetypes for the Control of Opinion Spread in Time-Varying Social Networks
Michael DeBuse, Sean Warnick
TL;DR
Addresses how controller archetypes steer opinion spread in time-varying networks with co-evolving topology. Uses a nonlinear, stochastic model on a time-varying graph $G=(V,E)$ with $X_{[k]}\in\mathbb{R}^{n\times m}$ and $A_{[k]}$, updated by $X_{[k+1]} = W(X_{[k]},A_{[k]})X_{[k]}$, and three archetypes driven by Hadamard power $\rho$. Demonstrates through simulations that Popular and Strategic agents can shift the network's opinion distribution depending on $\rho$ and edge formation probability $\epsilon$, while Stubborn acts as a baseline; shows practical deployment of Generative AI via an Opinion Inference Engine and content generation from opinion vectors. Raises ethical considerations anchored in established frameworks (Belmont, Menlo, ACM, NIST) and advocates for responsible AI governance and further study of cognitive security. Significance: provides insight into how automated agents and LLMs can be used to influence large-scale opinion dynamics and highlights the need for ethical safeguards.
Abstract
In this work we consider the impact of information spread in time-varying social networks, where agents request to follow other agents with aligned opinions while dropping ties to neighbors whose posts are too dissimilar to their own views. Opinion control and rhetorical influence has a very long history, employing various methods including education, persuasion, propaganda, marketing, and manipulation through mis-, dis-, and mal-information. The automation of opinion controllers, however, has only recently become easily deployable at a wide scale, with the advent of large language models (LLMs) and generative AI that can translate the quantified commands from opinion controllers into actual content with the appropriate nuance. Automated agents in social networks can be deployed for various purposes, such as breaking up echo chambers, bridging valuable new connections between agents, or shaping the opinions of a target population -- and all of these raise important ethical concerns that deserve serious attention and thoughtful discussion and debate. This paper attempts to contribute to this discussion by considering three archetypal influencing styles observed by human drivers in these settings, comparing and contrasting the impact of these different control methods on the opinions of agents in the network. We will demonstrate the efficacy of current generative AI for generating nuanced content consistent with the command signal from automatic opinion controllers like these, and we will report on frameworks for approaching the relevant ethical considerations.