The Impact of Recommendation Systems on Opinion Dynamics: Microscopic versus Macroscopic Effects
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Author / Producer
Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
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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.
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Publication status
published
Editor
Book title
2023 62nd IEEE Conference on Decision and Control (CDC)
Journal / series
Volume
Pages / Article No.
4824 - 4829
Publisher
IEEE
Event
62nd IEEE Conference on Decision and Control (CDC 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09478 - Dörfler, Florian / Dörfler, Florian
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)
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