Learning from Viral Content

TL;DR

This paper models user interactions with shared news stories on social media, analyzing how viral content influences information accuracy and the potential for misleading steady states, with implications for platform design.

Contribution

It introduces an equilibrium model of social media sharing that examines the impact of viral content and sampling algorithms on information accuracy and steady states.

Findings

Viral stories can enhance information aggregation.

Viral stories can lead to persistent misinformation.

Sampling algorithms influence the prevalence of wrong stories.

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, 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.