How do social media feed algorithms affect attitudes and behavior in an election campaign?

We investigated the effects of Facebook’s and Instagram’s feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users’ on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.

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Details

Published: Jul 28, 2023
Vol/Issue: 381(6656)
Pages: 398-404

Authors

A

Andrew M. Guess

Department of Politics and School of Public and International Affairs, Princeton University, Princeton, NJ, USA.

N

Neil Malhotra

Graduate School of Business, Stanford University, Stanford, CA, USA.

J

Jennifer Pan

Department of Communication, Stanford University, Stanford, CA, USA.

P

Pablo Barberá

Meta, Menlo Park, CA, USA.

H

Hunt Allcott

Stanford Doerr School of Sustainability, Stanford University, Stanford, CA, USA.

T

Taylor Brown

Meta, Menlo Park, CA, USA.

A

Adriana Crespo-Tenorio

Meta, Menlo Park, CA, USA.

D

Drew Dimmery

Meta, Menlo Park, CA, USA.; Research Network Data Science, University of Vienna, Vienna, Austria.

D

Deen Freelon

UNC Hussman School of Journalism and Media, University of North Carolina at Chapel Hill, Chapel, NC, USA.

M

Matthew Gentzkow

Department of Economics, Stanford University, Stanford, CA, USA.

S

Sandra Gonzalez-Bailon

Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA.

E

Edward Kennedy

Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.

Y

Young Mie Kim

School of Journalism and Mass Communication, University of Wisconsin–Madison, Madison, WI, USA.

D

David Lazer

Network Science Institute, Northeastern University, Boston, MA, USA.

D

Devra Moehler

Meta, Menlo Park, CA, USA.

B

Brendan Nyhan

Department of Government, Dartmouth College, Hanover, NH, USA.

C

Carlos Velasco Rivera

Meta, Menlo Park, CA, USA.

J

Jaime Settle

Department of Government, William & Mary, Williamsburg, VA, USA.

D

Daniel Robert Thomas

Meta, Menlo Park, CA, USA.

E

EMILY THORSON

Department of Political Science, Syracuse University, Syracuse, NY, USA.

R

Rebekah Tromble

School of Media and Public Affairs and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC, USA.

A

Arjun Wilkins

Meta, Menlo Park, CA, USA.

M

Magdalena Wojcieszak

Department of Communication, University of California, Davis, Davis, CA, USA.; Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, Netherlands.

B

Beixian Xiong

Meta, Menlo Park, CA, USA.

C

Chad Kiewiet de Jonge

Meta, Menlo Park, CA, USA.

A

Annie Franco

Meta, Menlo Park, CA, USA.

W

Winter Mason

Meta, Menlo Park, CA, USA.

N

Natalie Jomini Stroud

Moody College of Communication and Center for Media Engagement, University of Texas at Austin, Austin, TX, USA.

J

Joshua A. Tucker

Wilf Family Department of Politics and Center for Social Media and Politics, New York University, New York, NY, USA.

Cite This Article

Andrew M. Guess, Neil Malhotra, Jennifer Pan, et al. (2023). How do social media feed algorithms affect attitudes and behavior in an election campaign?. Science, 381(6656), 398-404. https://doi.org/10.1126/science.abp9364

How do social media feed algorithms affect attitudes and behavior in an election campaign?

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