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


Journal

Science (New York, N.Y.)
ISSN: 1095-9203
Titre abrégé: Science
Pays: United States
ID NLM: 0404511

Informations de publication

Date de publication:
28 07 2023
Historique:
medline: 31 7 2023
pubmed: 27 7 2023
entrez: 27 7 2023
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 37498999
doi: 10.1126/science.abp9364
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

398-404

Auteurs

Andrew M Guess (AM)

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

Neil Malhotra (N)

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

Jennifer Pan (J)

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

Hunt Allcott (H)

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

Adriana Crespo-Tenorio (A)

Meta, Menlo Park, CA, USA.

Drew Dimmery (D)

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

Deen Freelon (D)

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

Matthew Gentzkow (M)

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

Sandra González-Bailón (S)

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

Edward Kennedy (E)

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

Young Mie Kim (YM)

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

David Lazer (D)

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

Brendan Nyhan (B)

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

Carlos Velasco Rivera (CV)

Meta, Menlo Park, CA, USA.

Jaime Settle (J)

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

Daniel Robert Thomas (DR)

Meta, Menlo Park, CA, USA.

Emily Thorson (E)

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

Rebekah Tromble (R)

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

Arjun Wilkins (A)

Meta, Menlo Park, CA, USA.

Magdalena Wojcieszak (M)

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

Beixian Xiong (B)

Meta, Menlo Park, CA, USA.

Chad Kiewiet de Jonge (CK)

Meta, Menlo Park, CA, USA.

Annie Franco (A)

Meta, Menlo Park, CA, USA.

Natalie Jomini Stroud (NJ)

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

Joshua A Tucker (JA)

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

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Classifications MeSH