Stochastic Models of Misinformation Distribution in Online Social Networks

TL;DR

This paper experimentally studies how misinformation spreads in online social networks, classifies network topologies, and proposes an algorithm to identify percolation clusters, enhancing understanding of misinformation dynamics.

Contribution

It introduces a method to classify OSN topologies and presents a new algorithm for detecting percolation clusters in social graphs, linking network structure to misinformation spread.

Findings

Network topology influences misinformation distribution.

Algorithm effectively identifies percolation clusters.

Classification helps predict misinformation spread patterns.

Abstract

This report contains results of an experimental study of the distribution of misinformation in online social networks (OSNs). We consider the classification of the topologies of OSNs and analyze the parameters identified in order to relate the topology of a real network with one of the classes. We propose an algorithm for conducting a search for the percolation cluster in the social graph.