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

Author(s): Andrew M. Guess ¹ , Neil Malhotra ² , Jennifer Pan ³ , Pablo Barberá ⁴ , Hunt Allcott ⁵ , Taylor Brown ⁴ , Adriana Crespo-Tenorio ⁴ , Drew Dimmery ⁴ ^, ⁶ , Deen Freelon ⁷ , Matthew Gentzkow ⁸ , Sandra González-Bailón ⁹ , Edward Kennedy ¹⁰ , Young Mie Kim ¹¹ , David Lazer ¹² , Devra Moehler ⁴ , Brendan Nyhan ¹³ , Carlos Velasco Rivera ⁴ , Jaime Settle ¹⁴ , Daniel Robert Thomas ⁴ , Emily Thorson ¹⁵ , Rebekah Tromble ¹⁶ , Arjun Wilkins ⁴ , Magdalena Wojcieszak ¹⁷ ^, ¹⁸ , Beixian Xiong ⁴ , Chad Kiewiet de Jonge ⁴ , Annie Franco ⁴ , Winter Mason ⁴ , Natalie Jomini Stroud ¹⁹ , Joshua A. Tucker ²⁰

Publication date Created: July 28 2023

Publication date (Print): July 28 2023

Journal: Science

Publisher: American Association for the Advancement of Science (AAAS)

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Abstract

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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Most cited references 56

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Dissecting racial bias in an algorithm used to manage the health of populations

Ziad Obermeyer, Brian Powers, Christine Vogeli … (2019)

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

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Adaptive linear step-up procedures that control the false discovery rate

Y Benjamini, A. M. Krieger, D Yekutieli (2006)

0 comments Cited 626 times – based on 0 reviews      Review now

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Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects

Michael Anderson (2008)

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Author and article information

Contributors

Andrew M. Guess: (View ORCID Profile)

Neil Malhotra: (View ORCID Profile)

Jennifer Pan: (View ORCID Profile)

Pablo Barberá: (View ORCID Profile)

Taylor Brown: (View ORCID Profile)

Drew Dimmery: (View ORCID Profile)

Deen Freelon: (View ORCID Profile)

Matthew Gentzkow: (View ORCID Profile)

Sandra González-Bailón: (View ORCID Profile)

Edward Kennedy: (View ORCID Profile)

Young Mie Kim: (View ORCID Profile)

David Lazer: (View ORCID Profile)

Devra Moehler: (View ORCID Profile)

Brendan Nyhan: (View ORCID Profile)

Jaime Settle: (View ORCID Profile)

Emily Thorson: (View ORCID Profile)

Rebekah Tromble: (View ORCID Profile)

Magdalena Wojcieszak: (View ORCID Profile)

Winter Mason: (View ORCID Profile)

Natalie Jomini Stroud: (View ORCID Profile)

Joshua A. Tucker: (View ORCID Profile)

Journal

Title: Science

Abbreviated Title: Science

Publisher: American Association for the Advancement of Science (AAAS)

ISSN (Print): 0036-8075

ISSN (Electronic): 1095-9203

Publication date Created: July 28 2023

Publication date (Print): July 28 2023

Volume: 381

Issue: 6656

Pages: 398-404

Affiliations

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

[2 ]Graduate School of Business, Stanford University, Stanford, CA, USA.

[3 ]Department of Communication, Stanford University, Stanford, CA, USA.

[4 ]Meta, Menlo Park, CA, USA.

[5 ]Stanford Doerr School of Sustainability, Stanford University, Stanford, CA, USA.

[6 ]Research Network Data Science, University of Vienna, Vienna, Austria.

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

[8 ]Department of Economics, Stanford University, Stanford, CA, USA.

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

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

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

[12 ]Network Science Institute, Northeastern University, Boston, MA, USA.

[13 ]Department of Government, Dartmouth College, Hanover, NH, USA.

[14 ]Department of Government, William & Mary, Williamsburg, VA, USA.

[15 ]Department of Political Science, Syracuse University, Syracuse, NY, USA.

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

[17 ]Department of Communication, University of California, Davis, Davis, CA, USA.

[18 ]Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, Netherlands.

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

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

Article

DOI: 10.1126/science.abp9364

PubMed ID: 37498999

SO-VID: c96f303f-80a8-4e87-a534-ae0e1fa63701

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