July 28, 2020, 14:00–15:00
Room Zoom meeting
Economics of Platforms Seminar
Abstract
We run a field experiment to quantify the economic returns to data and informational externalities associated with algorithmic recommendation in the context of online news. Our results show that personalized recommendation can outperform human curation in terms of user engagement, though this crucially depends on the amount of personal data. Limited individual data or breaking news leads the editor to outperform the algorithm. Additional data helps algorithmic performance but decreasing economic returns set in rapidly. Investigating informational externalities highlights that personalized recommendation reduces consumption diversity. Moreover, users associated with lower levels of digital literacy and more extreme political views engage more with algorithmic recommendations.