MICRO/THEORY: Algorithmic Attention and Content Creation on Social Media Platforms; Professor Fei Li (University of North Carolina Chapel Hill)

This paper develops a theory framework to examine how an ads-funded social media platform allocates attention through recommendation algorithms and how this in turn shapes content creation and consumption. Producers, heterogeneous in ability to create contents, are one side of the platform while consumers, heterogeneous in appreciating the contents, are the other side. The optimal algorithm filters out low-ability producers but guarantees a minimal readership for those who produce, while propagating viral content for high-ability ones. The algorithm deliberately assigns excessive attention in unprofitable matches so as to leverage the two-sided network effect. We show the source of the inefficiencies of the algorithm by contrasting it with a welfare-maximizing benchmark. User privacy protection benefits the consumers but may hurt the producers.