Modeling confirmation bias and polarization

TL;DR

This paper introduces new mathematical models to simulate online social debates, capturing confirmation bias and polarization phenomena, and demonstrates their ability to reproduce stable coexistence of opposing opinions.

Contribution

It develops two novel variations of the Bounded Confidence Model that better explain real-world opinion coexistence and polarization dynamics.

Findings

Models can reproduce stable coexistence of opposing opinions.

Rewiring and unbounded confidence models capture polarization phenomena.

Mean field approximation provides analytical insights.

Abstract

Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models.