Template-Type: ReDIF-Paper 1.0
Title: Feed for good? On the effects of personalization algorithms in social media 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. This algorithm overexploits similarities between users,
locking them in echo chambers. Moreover, learning vanishes as platform size grows
large. As this is far from ideal, we explore alternatives. The reverse-chronological
algorithm that social platforms reincorporated after the DSA was enacted turns out
to be insufficient, so we construct the "breaking-echo-chambers" algorithm, which
improves learning by promoting opposite viewpoints. Finally, we advocate for horizontal
interoperability as a regulatory measure to align platform incentives with
social welfare. By eliminating platform-specific network effects, interoperability incentivizes
platforms to adopt algorithms that maximize user well-being.

Note: 
Length: 43
Creation-Date: 2024-08
Revision-Date: 2024-12
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp580
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2024_580v2