Modelling the Closed Loop Dynamics Between a Social Media Recommender System and Users' Opinions

TL;DR

This paper develops a mathematical model to analyze how social media recommender systems influence user opinions, leading to polarization or radicalization, and explores strategies like viral content to mitigate these effects.

Contribution

It introduces a coupled dynamic model of recommender systems and user opinions, and uses Monte Carlo simulations to study their mutual influence and impact on polarization.

Findings

Certain opinion distributions are more prone to polarization.

Many content types are ineffective in changing opinions.

Viral content can help reduce opinion polarization.

Abstract

This paper proposes a mathematical model to study the coupled dynamics of a Recommender System (RS) algorithm and content consumers (users). The model posits that a large population of users, each with an opinion, consumes personalised content recommended by the RS. The RS can select from a range of content to recommend, based on users' past engagement, while users can engage with the content (like, watch), and in doing so, users' opinions evolve. This occurs repeatedly to capture the endless content available for user consumption on social media. We employ a campaign of Monte Carlo simulations to study how recommender systems influence users' opinions, and in turn how users' opinions shape the subsequent recommended content. Both the performance of the RS (e.g., how users engage with the content) and the polarisation and radicalisation of users' opinions are of interest. We find that different opinion distributions are more susceptible to becoming polarised than others, many content stances are ineffective in changing user opinions, and creating viral content is an effective measure in combating polarisation of opinions. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.