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

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 using this model to study how recommender systems influence users' opinions, and in turn how users' opinions shape the subsequent recommended content. We take an interest in both the performance of the RS (e.g., how users engage with the content) and the user's opinions, focusing on polarisation and radicalisation of opinions. 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.