Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2022-12-22
Number: 22-097/II
Author-Name: Saeed Badri
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Bernd Heidergott
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Ines Lindner
Author-Workplace-Name: Vrije Universiteit Amsterdam
Title: Na?ve Learning in Social Networks with Fake News: Bots as a Singularity
Abstract: We study the impact of bots on social learning in a social network setting.
Regular agents receive independent noisy signals about the true value of a variable and
then communicate in a network. They na¨?vely update beliefs by repeatedly taking weighted
averages of neighbors’ opinions. Bots are agents in the network that spread fake news by
disseminating biased information. Our main contributions are threefold. (1) We show that
the consensus of the network is a mapping of the interaction rate between the agents and
bots and is discontinuous at zero mass of bots. This implies that even a comparatively
“infinitesimal” small number of bots still has a sizeable impact on the consensus and hence
represents an obstruction to the “wisdom of crowds”. (2) We prove that the consensus
gap induced by the marginal presence of bots depends neither on the agent network or bot
layout nor on the assumed connection structure between agents and bots. (3) We show that
before the ultimate (and bot-infected) consensus is reached, the network passes through a
quasi-stationary phase which has the potential to mitigate the harmful impact of bots.
Classification-JEL: D83, D85, Z13
Keywords: Fake news, Misinformation, Social networks, Social Media, Wisdom of Crowds
File-URL: https://papers.tinbergen.nl/22097.pdf
File-Format: application/pdf
File-Size: 454.123 bytes
Handle: RePEc:tin:wpaper:20220097