Feed for good? On the effects of personalization algorithms in social media platforms

Miguel Risco () and Manuel Lleonart-Anguix ()

CRC TR 224 Discussion Paper Series from University of Bonn and University of Mannheim, Germany

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.

Keywords: personalized feed; social learning; network effects; interoperability (search for similar items in EconPapers)
JEL-codes: D43 D85 L15 L86 (search for similar items in EconPapers)
Pages: 43
Date: 2024-08, Revised 2024-12
New Economics Papers: this item is included in nep-mic and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:bon:boncrc:crctr224_2024_580v2

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