Reversible bootstrap percolation: Fake news and fact checking

TL;DR

This paper introduces a reversible bootstrap percolation model that captures hysteresis effects in opinion dynamics, providing insights into fake news spread and the limitations of fact checking.

Contribution

It develops a reversible bootstrap percolation framework with hysteresis, modeling fake news spread and demonstrating that removing sources may not always reverse beliefs.

Findings

Reversible bootstrap percolation exhibits hysteresis similar to phase transitions.

Removing fake news sources may be insufficient to change public opinion.

The model explains the persistence of false beliefs despite fact checking.

Abstract

Bootstrap percolation has been used to describe opinion formation in society and other social and natural phenomena. The formal equation of the bootstrap percolation may have more than one solution, corresponding to several stable fixed points of the corresponding iteration process. We construct a reversible bootstrap percolation process, which converges to these extra solutions displaying a hysteresis typical of discontinuous phase transitions. This process provides a reasonable model for fake news spreading and the effectiveness of fact checking. We show that sometimes it is not sufficient to discard all the sources of fake news in order to reverse the belief of a population that formed under the influence of these sources.