The Impact of Curation Algorithms on Social Network Content Quality and Structure



Curation algorithms are selection and ranking algorithms that social media platforms use to improve user experience. This paper analyzes the impact of curation algorithms on the number of friends consumers connect to and the quality of content created by producers. The model takes into account both vertical and horizontal differentiation and analyzes three different types of algorithms. The results show that without algorithmic curation, the number of friends an individual has and the quality of content on the platform are strategic complements. Introducing algorithmic curation makes consumers less selective in their follower lists when content quality is low. In equilibrium, producers of content receive lower payoffs because they enter into a contest leading to a prisoner's dilemma. The quality of content on the platform may increase if the marginal cost of producing this quality is high enough. Both of these effects may result theoretically in more diverse content consumption, but in equilibrium we find that a perfect filtering algorithm may reduce the horizontal distance of matched content resulting in a filter bubble. We identify an algorithm that focuses on filtering low quality items that results in higher quality of content as well as higher diversity under specific conditions.