  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, and then decide which stories to share.
The observed sample of stories depends on what predecessors share as well as
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.
