Breaking Echo Chambers with Personalized News

84 Pages Posted: 7 May 2019 Last revised: 31 Aug 2023

See all articles by Jiemai Wu

Jiemai Wu

The University of Sydney - School of Economics

John H. Nachbar

Washington University in St. Louis - Department of Economics

Date Written: August 30, 2023

Abstract

When a news aggregator platform such as Google News or Apple News selects personalized news for its user, will it select news that conforms to its user’s bias, thus creating an “echo chamber”? To answer this question, this paper studies a game between a click-maximizing platform and a rational Bayesian user who reads news for its instrumental value. We derived the conditions for the user to exhibit a preference for headlines that contradict her prior bias, which induces the platform to recommend prior-contradicting headlines. Common statistical distributions, including the Normal distribution, satisfy these conditions. Our theory helps explain why, empirically, some media pander to their consumers’ biases while others do the opposite, and why some consumers seek prior-conforming news while others seek news to challenge their views.

Keywords: media bias, personalized news, echo chambers, news aggregator, platform

JEL Classification: C72, D83, L82

Suggested Citation

Wu, Jiemai and Nachbar, John H., Breaking Echo Chambers with Personalized News (August 30, 2023). Available at SSRN: https://ssrn.com/abstract=3375352 or http://dx.doi.org/10.2139/ssrn.3375352

Jiemai Wu (Contact Author)

The University of Sydney - School of Economics ( email )

Social Sciences Building
Room 510
Sydney, NSW 2006
Australia

John H. Nachbar

Washington University in St. Louis - Department of Economics ( email )

One Brookings Drive
St. Louis, MO 63130
United States
314-935-5612 (Phone)
314-935-4156 (Fax)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
313
Abstract Views
1,411
Rank
189,833
PlumX Metrics