How do social media feed algorithms affect attitudes and behavior in an election campaign?-Reference-Cited by-同舟云学术

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

Published:2023-07-28 Issue:6656 Volume:381 Page:398-404
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Guess Andrew M.¹^ORCID,Malhotra Neil²^ORCID,Pan Jennifer³^ORCID,Barberá Pablo⁴^ORCID,Allcott Hunt⁵,Brown Taylor⁴^ORCID,Crespo-Tenorio Adriana⁴,Dimmery Drew⁴⁶^ORCID,Freelon Deen⁷^ORCID,Gentzkow Matthew⁸^ORCID,González-Bailón Sandra⁹^ORCID,Kennedy Edward¹⁰^ORCID,Kim Young Mie¹¹^ORCID,Lazer David¹²^ORCID,Moehler Devra⁴^ORCID,Nyhan Brendan¹³^ORCID,Rivera Carlos Velasco⁴,Settle Jaime¹⁴^ORCID,Thomas Daniel Robert⁴,Thorson Emily¹⁵^ORCID,Tromble Rebekah¹⁶^ORCID,Wilkins Arjun⁴,Wojcieszak Magdalena¹⁷¹⁸^ORCID,Xiong Beixian⁴,de Jonge Chad Kiewiet⁴,Franco Annie⁴,Mason Winter⁴^ORCID,Stroud Natalie Jomini¹⁹^ORCID,Tucker Joshua A.²⁰^ORCID

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

Link

https://www.science.org/doi/pdf/10.1126/science.abp9364

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

3. How social media shapes polarization

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

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

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Researching and countering misinformation in the Global South;Current Opinion in Psychology;2024-02

2. The effects of urbanization and social media use on individuals’ perceived social isolation;Online Journal of Communication and Media Technologies;2024-01-30

3. Beyond online disinformation: assessing national information resilience in four European countries;Humanities and Social Sciences Communications;2024-01-13

4. On the impossibility of breaking the echo chamber effect in social media using regulation;Scientific Reports;2024-01-11

5. Null effects of social media ads on voter registration: Three digital field experiments;Research & Politics;2024-01