Template-Type: ReDIF-Paper 1.0
Title: Feed for good? On the effects of personalization algorithms in social platforms
Author-Name: Miguel Risco
Author-Email: risco@uni-bonn.de
Author-Name: Manuel Lleonart-Anguix
Author-Email: manuel.lleonart@bse.eu

Classification-JEL: D43, D85, L15, L86
Keywords: personalized feed, social learning, network effects, interoperability

Abstract: This paper builds a theoretical model of communication and learning on a social
media platform, and describes the algorithm an engagement-maximizing platform
implements in equilibrium. Such algorithm excessively exploits similarity, locking
users in echo chambers. Moreover, learning vanishes as platform size grows large. As
this is far from ideal, we explore alternatives. The reverse-chronological algorithm
the DSA mandated to reincorporate turns out to be not good enough, so we build
the "breaking echo chambers" algorithm, a modification of the platform-optimal
algorithm that improves learning by promoting opposite thoughts. Additionally,
we seek a natural implementation path for the utilitarian optimal algorithm. This
is why we advocate for horizontal interoperability, which interoperability compels
platforms to compete based on algorithms. In the absence of platform-specific network
effects that entrench users within dominant platforms, the retention of user
bases hinges on implementing algorithms that outperform those of competitors.

Note: 
Length: 41
Creation-Date: 2024-08
Revision-Date: 
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp580
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2024_580