Our latest publication of 2025 “Consensus effects of social media synthetic influence groups on scale-free networks” has just appeared in Chaos, Solitons & Fractals.
Online platforms for social interactions are an essential part of modern society. With the advance of technology and the rise of AI algorithms, content is now filtered systematically, facilitating the formation of filter bubbles. This work investigates the social consensus under limited visibility in a two-state majority-vote model on Barabási–Albert scale-free networks. In the consensus evolution, each individual assimilates the opinion of the majority of their neighbors with probability (1-q) and disagrees with chance q, known as the noise parameter. We define a visibility parameter as the probability of an individual considering the opinion of a neighbor at a given interaction. This parameter enables us to model the limited visibility phenomenon that produces synthetic neighborhoods in online interactions. We employ Monte Carlo simulations and finite-size scaling analysis to obtain the critical noise parameter as a function of the visibility and the network growth parameter. We also find the critical exponents of the system and validate the unitary relation for complex networks. Our analysis shows that installing and manipulating synthetic influence groups critically undermines consensus robustness, leading to serious consequences in the modern society.
Although this is not our main line of work, it is a type of problem I also enjoy working on. The results stem from the work of graduate student Giuliano Porciúncula, who is supervised by my colleague André L. M. Vilela at UPE.