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

Andrew M. Guess; Neil Malhotra; Jennifer Pan; Pablo Barberá; Hunt Allcott; Taylor Brown; Adriana Crespo-Tenorio; Drew Dimmery; Deen Freelon; Matthew Gentzkow; Sandra González-Bailón; Edward Kennedy; Young Mie Kim; David Lazer; Devra Moehler; Brendan Nyhan; Carlos Velasco Rivera; Jaime Settle; Daniel Robert Thomas; Emily Thorson; Rebekah Tromble; Arjun Wilkins; Magdalena Wojcieszak; Beixian Xiong; Chad Kiewiet De Jonge; Annie Franco; Winter Mason; Natalie Jomini Stroud; Joshua A. Tucker

doi:10.1126/science.abp9364

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

Andrew M. Guess, Neil Malhotra, Jennifer Pan, Pablo Barberá, Hunt Allcott, Taylor Brown, Adriana Crespo-Tenorio, Drew Dimmery, Deen Freelon, Matthew Gentzkow, Sandra González-Bailón, Edward Kennedy, Young Mie Kim, David Lazer, Devra Moehler, Brendan Nyhan, Carlos Velasco Rivera, Jaime Settle, Daniel Robert Thomas, Emily ThorsonRebekah Tromble, Arjun Wilkins, Magdalena Wojcieszak, Beixian Xiong, Chad Kiewiet De Jonge, Annie Franco, Winter Mason, Natalie Jomini Stroud, Joshua A. Tucker

Princeton School of Public and International Affairs

Research output: Contribution to journal › Article › peer-review

114 Scopus citations

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.

Original language	English (US)
Pages (from-to)	398-404
Number of pages	7
Journal	Science
Volume	381
Issue number	6656
DOIs	https://doi.org/10.1126/science.abp9364
State	Published - Jul 28 2023

All Science Journal Classification (ASJC) codes

General

Access to Document

10.1126/science.abp9364

Cite this

Guess, A. M., Malhotra, N., Pan, J., Barberá, P., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., Freelon, D., Gentzkow, M., González-Bailón, S., Kennedy, E., Kim, Y. M., Lazer, D., Moehler, D., Nyhan, B., Rivera, C. V., Settle, J., Thomas, D. R., ... Tucker, J. A. (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

@article{eee20a4dc92c4cccbe48c996e73290b2,

title = "How do social media feed algorithms affect attitudes and behavior in an election campaign?",

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.",

author = "Guess, {Andrew M.} and Neil Malhotra and Jennifer Pan and Pablo Barber{\'a} and Hunt Allcott and Taylor Brown and Adriana Crespo-Tenorio and Drew Dimmery and Deen Freelon and Matthew Gentzkow and Sandra Gonz{\'a}lez-Bail{\'o}n and Edward Kennedy and Kim, {Young Mie} and David Lazer and Devra Moehler and Brendan Nyhan and Rivera, {Carlos Velasco} and Jaime Settle and Thomas, {Daniel Robert} and Emily Thorson and Rebekah Tromble and Arjun Wilkins and Magdalena Wojcieszak and Beixian Xiong and {De Jonge}, {Chad Kiewiet} and Annie Franco and Winter Mason and Stroud, {Natalie Jomini} and Tucker, {Joshua A.}",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors.",

year = "2023",

month = jul,

day = "28",

doi = "10.1126/science.abp9364",

language = "English (US)",

volume = "381",

pages = "398--404",

journal = "Science",

issn = "0036-8075",

publisher = "American Association for the Advancement of Science",

number = "6656",

}

Guess, AM, Malhotra, N, Pan, J, Barberá, P, Allcott, H, Brown, T, Crespo-Tenorio, A, Dimmery, D, Freelon, D, Gentzkow, M, González-Bailón, S, Kennedy, E, Kim, YM, Lazer, D, Moehler, D, Nyhan, B, Rivera, CV, Settle, J, Thomas, DR, Thorson, E, Tromble, R, Wilkins, A, Wojcieszak, M, Xiong, B, De Jonge, CK, Franco, A, Mason, W, Stroud, NJ & Tucker, JA 2023, 'How do social media feed algorithms affect attitudes and behavior in an election campaign?', Science, vol. 381, no. 6656, pp. 398-404. https://doi.org/10.1126/science.abp9364