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Learning from Viral Content

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  • Krishna Dasaratha

    (Boston University)

  • Kevin He

    (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.

Suggested Citation

  • Krishna Dasaratha & Kevin He, 2025. "Learning from Viral Content," PIER Working Paper Archive 25-021, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:25-021
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