Depolarization of echo chambers by random dynamical nudge

TL;DR

This paper proposes a simple, random feedback mechanism called RDN that can prevent and depolarize echo chambers in social networks, promoting neutral consensus without surveillance.

Contribution

It introduces the RDN method, a novel, non-intrusive feedback approach that effectively depolarizes opinions and bridges communities in opinion dynamics models.

Findings

RDN leads to a unimodal, neutral opinion distribution.

RDN prevents formation of echo chambers.

Effective in depolarizing existing echo chambers.

Abstract

In social networks, users often engage with like-minded peers. This selective exposure to opinions might result in echo chambers, i.e., political fragmentation and social polarization of user interactions. When echo chambers form, opinions have a bimodal distribution with two peaks on opposite sides. In certain issues, where either extreme positions contain a degree of misinformation, neutral consensus is preferable for promoting discourse. In this paper, we use an opinion dynamics model that naturally forms echo chambers in order to find a feedback mechanism that bridges these communities and leads to a neutral consensus. We introduce random dynamical nudge (RDN), which presents each agent with input from a random selection of other agents' opinions and does not require surveillance of every person's opinions. Our computational results in two different models suggest that the RDN leads to a unimodal distribution of opinions centered around the neutral consensus. Furthermore, the RDN is effective both for preventing the formation of echo chambers and also for depolarizing existing echo chambers. Due to the simple and robust nature of the RDN, social media networks might be able to implement a version of this self-feedback mechanism, when appropriate, to prevent the segregation of online communities on complex social issues.