Algorithmic Assortative Matching on a Digital Social Medium

Kristian Lopez Vargas, Julian Runge () and Ruizhi Zhang ()
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Julian Runge: Duke University, Durham, North Carolina 27708; Rheinisch-Westfälische Technische Hochschule Aachen University, 52062 Aachen, Germany
Ruizhi Zhang: University of California, Santa Cruz, Santa Cruz, California 95064

Information Systems Research, 2022, vol. 33, issue 4, 1138-1156

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.

Keywords: assortative matching; social media; field experiments; freemium; systems design and implementation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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