The Editor vs. the Algorithm: Returns to Data and Externalities in Online News

Jörg Claussen, Christian Peukert () and Ananya Sen

No 8012, CESifo Working Paper Series from CESifo

Abstract: We run a field experiment to quantify the economic returns to data and informational ex-ternalities associated with algorithmic recommendation relative to human curation 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 diminishing 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.

Keywords: field experiment; economics of AI; returns to data; filter bubbles (search for similar items in EconPapers)
JEL-codes: J24 L51 L82 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-big, nep-exp, nep-ict and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_8012

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