Algorithmic Assortative Matching on a Digital Social Medium

Abstract

Humans are increasingly interacting in and operating their daily lives through structured digital and virtual environments, mainly through apps that provide media for sharing photos, messaging, gaming, collaborating, or video watching. Most of these digital environments are offered under “freemium” pricing to facilitate adoption and network effects. In these settings, users' early social interaction and experience often have a substantial impact on their longer term behavior. On this background, we study the impact of an algorithmic system that matches new users to existing communities in an assortative manner. We devise a machine learning-based matching system that identifies users with high expected value and provides them the option to join highly active, in terms of engagement and expenditure, teams. We deploy this mechanism experimentally in a digital social game and find that it significantly increases user engagement, spending, and socialization. This finding holds for more active communities and overall. Teams matched with low-activity new users are negatively impacted, leading to an overall more segregated social environment. We argue that social experience and social behavior in groups are likely mechanisms that drive the impact of the matching system.