Algorithmic Assortative Matching on a Digital Social Medium

Online algorithms recommend “people we may know” and “content we may like.” Inherent in these recommendations is a notion of positive assortativity in which the people and content being suggested to us match our own preferences and beliefs. In this paper, we focus on such tacit (i.e., behind the scenes) algorithmic facilitation of assortativity at work across digital platforms and social media. To investigate the effects that it has on human online relating and behavior, we conduct a large-scale field experiment in a mobile social game in which we switch algorithmic assortative matching between new users and existing communities on and off over the course of six weeks. With the help of model-based analysis, we find such assortative matching to increase firm profits (measured as user engagement and monetization) via increased sociality (measured as user messaging). Results further show that such behind-the-scenes algorithmic matching leads to a segregating path between engaged and marginal online communities, further marginalizing less engaged and connected users. Our findings, hence, pinpoint a conflict between profit-centered and societally equitable management of online platforms and are important toward more algorithmic transparency and fairness as online algorithms structure ever larger parts of human life.