Curation Algorithms and Filter Bubbles in Social Networks
45 Pages Posted: 11 Oct 2016 Last revised: 25 Sep 2019
Date Written: September 21, 2019
Social platforms often use curation algorithms to match content to user tastes. Although designed to improve user experience, these algorithms have been blamed for increased polarization of consumed content. We analyze how curation algorithms impact the number of friends users follow and the quality of content generated on the network, taking into account horizontal and vertical differentiation. Although algorithms increase polarization for fixed networks, when they indirectly influence network connectivity and content quality their impact on polarization and segregation is less clear.
We find that network connectivity and content quality are strategic complements, and that introducing curation makes consumers less selective and increases connectivity. In equilibrium, content creators receive lower payoffs because they enter into a contest leading to a prisoner’s dilemma.
Filter bubbles are not always a consequence of curation algorithms. A perfect filtering algorithm increases content polarization and creates a filter bubble when the marginal cost of quality is low, while an algorithm focused on vertical content quality increases connectivity as well as lowers polarization and does not create a filter bubble. Consequently, although user surplus can increase through curating and encouraging high quality content, the type of algorithm used matters for the unintended consequence of creating a filter bubble.
Keywords: Social Media, Content Filtering, Ranking, Filter Bubble, Algorithmic Curation, Game Theory
JEL Classification: D85, M31, L14, C72
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