Influence Networks: Bayesian Modeling and Diffusion

TL;DR

This paper introduces a Bayesian latent space model for influence networks that quantifies individual influence and models idea diffusion, demonstrated on Twitter data and validated through simulations.

Contribution

It presents a novel reparameterization of Bayesian influence models and a new mechanism to characterize idea diffusion based on latent social characteristics.

Findings

Quantifies influence capacity of individuals in social networks.

Models diffusion of ideas with a new Bayesian approach.

Validated with Twitter data and simulation studies.

Abstract

In this article, we make an innovative adaptation of a Bayesian latent space model based on projections in a novel way to analyze influence networks. By appropriately reparameterizing the model, we establish a formal metric for quantifying each individual's influencing capacity and estimating their latent position embedded in a social space. This modeling approach introduces a novel mechanism for fully characterizing the diffusion of an idea based on the estimated latent characteristics. It assumes that each individual takes the following states: Unknown, undecided, supporting, or rejecting an idea. This approach is demonstrated using a influence network from Twitter (now $\mathbb{X}$) related to the 2022 Tax Reform in Colombia. An exhaustive simulation exercise is also performed to evaluate the proposed diffusion process.