A meritocratic network formation model for the rise of
social media influencers
Abstract Many of today’s most used online social networks such as Instagram, Youtube, Twitter, or Twitch are based on User-Generated Content (UGC), and the exploration of this content is enhanced by the integrated search engines. Prior multidisciplinary effort on studying social network formation processes has privileged topological elements or socio-strategic incentives. Here, we propose an untouched meritocratic approach inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We statistically and numerically analyze the network equilibria properties: while the expected outdegree of the nodes remains bounded by the logarithm of the network size, the expected indegree follows a Zipf’s law with respect to the quality ranking. Notably, our quality-based mechanism provides an intuitive explanation of the origin of the Zipf’s regularity in growing social networks. Our theoretical results are empirically validated against large data-sets collected from Twitch, a fast-growing platform for online gamers.