The Impact of Recommendation Systems on Opinion Dynamics: Microscopic versus Macroscopic Effects

TL;DR

This paper investigates how recommendation systems influence individual opinions and overall population opinion distribution, revealing that personal opinion shifts can occur without detectable changes at the population level.

Contribution

It introduces a new model analyzing the microscopic and macroscopic effects of recommendation systems on opinion dynamics, combining analytical and numerical methods.

Findings

Individual opinions can significantly shift without changing population opinion distribution.

Recommendation systems may distort personal opinions even when population surveys show no change.

Shifts at the micro-level do not necessarily reflect macro-level opinion dynamics.

Abstract

Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating through the available options, there have been growing concerns regarding its repercussions on the users and their opinions. Examples of negative impacts include the emergence of filter bubbles and the amplification of users' confirmation bias, which can cause opinion polarization and radicalization. In this paper, we study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic (i.e., at the level of a homogenous population) perspective. Specifically, we build on recent work on the interactions between opinion dynamics and recommendation systems to propose a model for this closed loop, which we then study both analytically and numerically. Among others, our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population. In particular, even in settings where the opinion distribution appears unaltered (e.g., measured via surveys across the population), the opinion of individual users might be significantly distorted by the recommendation system.