Depolarization of opinions on social networks through random nudges

TL;DR

This paper introduces a framework for mildly nudging social network agents to form random connections, effectively reducing polarization and echo chambers, while identifying optimal nudging levels to prevent radicalization.

Contribution

It presents a non-intrusive method for opinion depolarization in social networks by using random nudges, improving upon traditional models that overlook polarization phenomena.

Findings

Mild nudges significantly reduce polarization and echo chambers.

Large nudges may cause radicalization, highlighting the need for optimal levels.

Optimal nudge factor balances depolarization and radicalization prevention.

Abstract

Polarization of opinions has been empirically noted in many online social network platforms. Traditional models of opinion dynamics, based on statistical physics principles, do not account for the emergence of polarization and echo chambers in online network platforms. A recently introduced opinion dynamics model that incorporates the homophily factor -- the tendency of agents to connect with those holding similar opinions as their own -- captures polarization and echo chamber effects. In this work, we provide a non-intrusive framework for mildly nudging agents in an online community to form random connections. This is shown to lead to significant depolarization of opinions and decrease the echo chamber effects. Remarkably, even a mild nudge is seen to be effective in avoiding polarization, though a large nudge leads to another undesirable effect, namely, radicalization. Further, we obtain the optimal nudge factor to avoid the extremes of polarization and radicalization outcomes.