Can a Single Line of Code Change Society? The Systemic Risks of Optimizing Engagement in Recommender Systems on Global Information Flow, Opinion Dynamics and Social Structures

As the last few years have seen an increase in both online hostility and polarization, we need to move beyond the fact-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed by Very Large Online Platforms (VLOPs) such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive bias on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric, to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to a polarization of the opinion landscape, to a concentration of social power in the hands of the most toxic users and to an overexposure of users to negative content (up to 300% for some of them), a phenomenon called algorithmic negativity bias. Toxic users are more than twice as numerous in the top 1% of the most influential users than in the overall population. Overall, our results highlight the systemic risks generated by certain implementations of recommender systems and the urgent need to comprehensively identify implementations of recommender systems harmful to individuals and society. This is a necessary step in setting up future regulations for systemic SNSs, such as the European Digital Market Act.