Learning from Viral Content

Krishna Dasaratha () and Kevin He ()
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Krishna Dasaratha: Boston University
Kevin He: University of Pennsylvania

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: We study learning on social media with an equilibrium model of users interacting with shared news stories. Rational users arrive sequentially, observe an original story (i.e., a private signal) and a sample of predecessors’ stories in a news feed, then decide which stories to share. The observed sample of stories is jointly determined by predecessors’ sharing behavior and the sampling algorithm generating news feeds. We focus on how often this algorithm selects more viral (i.e., widely shared) stories. Showing users viral stories can increase information aggregation, but it can also generate steady states where most shared stories are wrong. These misleading steady states self-perpetuate, as users who observe wrong stories develop wrong beliefs, and thus rationally continue to share them. Finally, we describe several consequences for platform design and robustness.

Keywords: social learning; selective equilibrium sharing; social media; platform design; endogenous virality (search for similar items in EconPapers)
Pages: 69 pages
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:25-021

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