Learning through the Grapevine: The Impact of Noise and the Breadth and Depth of Social Networks

Abstract

We study how communication platforms can improve social learning without censoring or fact-checking messages, when they have members who deliberately and/or inadvertently distort information. We analyze message fidelity as a function of social network depth (how many times information is relayed) and breadth (the number of relay chains accessed). Message fidelity can be improved by capping depth or, if that is not possible, limiting breadth; e.g., by capping the number of people to whom someone can forward a given message. Although they reduce total communication, such caps increase the fraction of received messages originating closer to the receiver, thereby increasing the signal to noise ratio. We characterize how the expected number of true minus false messages depends on breadth and depth of the network and the noise structure.

Keywords: Social Learning, Communication, Noise, Mutation, Bias, Fake News

JEL Classification: D83, D85, L14, O12, Z13