Uncritical polarized groups: The impact of spreading fake news as fact in social networks

TL;DR

This paper introduces a minimal model to analyze the spread of fake news in social networks, focusing on polarized groups and providing methods to detect, quantify, and predict rumor propagation times.

Contribution

It presents a novel probabilistic model for rumor spread that accounts for polarized groups and offers empirical detection and prediction techniques.

Findings

Polarized groups can be detected and quantified from data.

The model provides a simple function to study rumor propagation over time.

Predictions of the time for rumors to reach a certain population percentage are achievable.

Abstract

The spread of ideas in online social networks is a crucial phenomenon to understand nowadays the proliferation of fake news and their impact in democracies. This makes necessary to use models that mimic the circulation of rumors. The law of large numbers as well as the probability distribution of contact groups allow us to construct a model with a minimum number of hypotheses. Moreover, we can analyze with this model the presence of very polarized groups of individuals (humans or bots) who spread a rumor as soon as they know about it. Given only the initial number of individuals who know any news, in a population connected by an instant messaging application, we first deduce from our model a simple function of time to study the rumor propagation. We then prove that the polarized groups can be detected and quantified from empirical data. Finally, we also predict the time required by any rumor to reach a fixed percentage of the population.