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

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

doi:10.1126/science.abp9364

Cited by 77 publications

(33 citation statements)

References 57 publications

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“…Fourth, although nudges to recom-119 mender systems boost the likelihood that users encounter and 120 consume news -and more diverse and cross-cutting news 121 -increased news consumption has no effects on the tested 122 attitudes, cognitions, and behaviors. These null effects do 123 not vary with users' baseline news exposure or partisanship 124 strength, findings which add to the growing body of evidence 125 that on-platform exposure has limited effects on attitudes 126 (46)(47)(48)(49). Crucially, the intervention does not decrease the time 127 spent and user engagement with the platform, which makes it 128 practically implementable.…”

Section: Nudging the Recommendation Algorithm Increases News Consumpt...mentioning

confidence: 89%

Nudging the Recommendation Algorithm Increases News Consumption and Diversity on YouTube

Haroon,

Yu,

Menchen-Trevino

et al. 2023

Preprint

Self Cite

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Recommendation algorithms profoundly shape users' attention and information consumption on social media platforms. This study introduces a computational intervention aimed at mitigating two key biases in algorithms by influencing the recommendation process. We tackle interest bias, or the tendency of the algorithm to recommend non-news, non-political information to the majority of users, and ideological bias, or the algorithm directing the politically interested and more strongly partisan users to one-sided congenial content. Employing a sock-puppet experiment alongside a month-long randomized experiment involving 2,142 frequent YouTube users, we investigate if nudging the algorithm by playing videos from quality, verified, and ideologically balanced news channels in the background increases recommendations to and consumption of quality news content. We additionally test if providing balanced news input to the algorithm promotes diverse and cross-cutting news recommendations and consumption. We find that nudging the algorithm significantly and sustainably enhances both recommendations and consumption of news and also successfully minimizes ideological biases in recommendations and consumption, particularly among conservative users. In fact, recommendations have stronger effects on users’ exposure than users’ exposure has on subsequent recommendations. In contrast, nudging the users themselves has no observable effects on news consumption. Increased news consumption, however, has no effects on democratic attitudes (e.g., knowledge, participation, polarization, misperceptions), adding to growing evidence of limited effects of on-platform exposure. The intervention does not adversely affect user engagement on YouTube, showcasing its potential for real-world implementation. These findings underscore the profound influence wielded by platform recommender algorithms on users' attention and information exposure.

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Section: Nudging the Recommendation Algorithm Increases News Consumpt...mentioning

confidence: 89%

Nudging the Recommendation Algorithm Increases News Consumption and Diversity on YouTube

Haroon,

Yu,

Menchen-Trevino

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To account for dependencies within spreaders, we report HC1 clusterrobust errors, clustered at the spreader level. 9 See Appendix Table 4 for regression results. The covariates were the variables listed in Appendix Table 3, plus two-way interactions between our variables of interest: initial exposure (measured by number of spreaders followed at T1), ideological strength (measured by absolute value of [2]'s ideology measure), and an indicator for liberal ideology (equal to 1 if our ideology [2] measure is below 0).…”

Section: Methodsmentioning

confidence: 99%

“…These studies suggest recommendation algorithms may amplify already-existing misinformation exposure. Yet, the largest field experiment to date on this topic suggests algorithms do not promote misinformation on Facebook and Instagram [9]. In a different domain (extremist content on YouTube), [4] also questions the significance of algorithmic effects and points to demand effects as a more important mechanism.…”

Section: Environmentalmentioning

confidence: 99%

The Dynamics of (Not) Unfollowing Misinformation Spreaders

Ashkinaze,

Gilbert,

Budak

2024

Proceedings of the ACM Web Conference 2024

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Many studies explore how people "come into" misinformation exposure. But much less is known about how people "come out of" misinformation exposure. Do people organically sever ties to misinformation spreaders? And what predicts doing so? Over six months, we tracked the frequency and predictors of ∼900K followers unfollowing ∼5K health misinformation spreaders on Twitter. We found that misinformation ties are persistent. Monthly unfollowing rates are just 0.52%. In other words, 99.5% of misinformation ties persist each month. Users are also 31% more likely to unfollow non-misinformation spreaders than they are to unfollow misinformation spreaders. Although generally infrequent, the factors most associated with unfollowing misinformation spreaders are (1) redundancy and (2) ideology. First, users initially following many spreaders, or who follow spreaders that tweet often, are most likely to unfollow later. Second, liberals are more likely to unfollow than conservatives. Overall, we observe a strong persistence of misinformation ties. The fact that users rarely unfollow misinformation spreaders suggests a need for external nudges and the importance of preventing exposure from arising in the first place.

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“…The study period was selected to include the month of the election (November 2020), as well as the month before and the month after the election. Agenda-setting research suggests that news dynamics in the weeks before elections can significantly affect voting decisions (McCombs & Shaw, 1972), and related research (Guess et al, 2023) has analyzed similar time periods. Also, in the weeks after the election, news plays an important role in legitimizing election results.…”

Section: Methods and Datamentioning

confidence: 99%

Facebook’s News Feed Algorithm and the 2020 US Election

Bandy,

Diakopoulos

2023

Social Media + Society

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Facebook plays a substantial role in shaping exposure to news and information, a role that is particularly significant during democratic elections. This article aims to characterize Facebook’s contribution to news exposure during the 2020 US election. Using internet browsing data from 1.3 million desktop panelists in the United States, we analyze audience flows from Facebook to news and information publishers ( N = 3,247 websites) labeled according to quality (high or low) and geography (non-local or local). We find that Facebook ranked as the fourth largest external referral source to publishers. Our analysis further shows that, among the panel of 1.3 million US adults visiting these publishers via desktop computers, Facebook disproportionately amplified low-quality publishers and extended their audience reach during the study period. We also find evidence that Facebook’s intervention to improve “news ecosystem quality” by tweaking their News Feed algorithm was associated with fewer daily visits to low-quality publishers, but seemed to have a similar effect on other publishers and, according to Facebook-provided data, had little effect on month-over-month traffic. We discuss limitations, open questions about platform impact, and directions for future work.

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

Cited by 77 publications

References 57 publications

Nudging the Recommendation Algorithm Increases News Consumption and Diversity on YouTube

Nudging the Recommendation Algorithm Increases News Consumption and Diversity on YouTube

The Dynamics of (Not) Unfollowing Misinformation Spreaders

Facebook’s News Feed Algorithm and the 2020 US Election

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