The Impact of Recommendation Systems on Opinion Dynamics: Microscopic versus Macroscopic Effects
Open access
Date
2023Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000646182Publication status
publishedExternal links
Book title
2023 62nd IEEE Conference on Decision and Control (CDC)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
09478 - Dörfler, Florian / Dörfler, Florian
Funding
180545 - NCCR Automation (phase I) (SNF)
More
Show all metadata
ETH Bibliography
yes
Altmetrics