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

Author:

Guess Andrew M.1ORCID,Malhotra Neil2ORCID,Pan Jennifer3ORCID,Barberá Pablo4ORCID,Allcott Hunt5,Brown Taylor4ORCID,Crespo-Tenorio Adriana4,Dimmery Drew46ORCID,Freelon Deen7ORCID,Gentzkow Matthew8ORCID,González-Bailón Sandra9ORCID,Kennedy Edward10ORCID,Kim Young Mie11ORCID,Lazer David12ORCID,Moehler Devra4ORCID,Nyhan Brendan13ORCID,Rivera Carlos Velasco4,Settle Jaime14ORCID,Thomas Daniel Robert4,Thorson Emily15ORCID,Tromble Rebekah16ORCID,Wilkins Arjun4,Wojcieszak Magdalena1718ORCID,Xiong Beixian4,de Jonge Chad Kiewiet4,Franco Annie4,Mason Winter4ORCID,Stroud Natalie Jomini19ORCID,Tucker Joshua A.20ORCID

Affiliation:

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.

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.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference75 articles.

1. E. Pariser The Filter Bubble: How the New Personalized Web is Changing What We Read and How We Think (Penguin 2011).

2. Sorting the News: How Ranking by Popularity Polarizes Our Politics

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4. C. O’Neil Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown 2016).

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