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Algorithmic amplification of political content
paperAuthors
Nyhan, Brendan·Settle, Jaime·Thorson, Emily·Wojcieszak, Magdalena·Barberá, Pablo·Chen, Annie Y.·Allcott, Hunt·Brown, Taylor·Crespo-Tenorio, Adriana·Dimmery, Drew·Freelon, Deen·Gentzkow, Matthew·González-Bailón, Sandra·Guess, Andrew M.·Kennedy, Edward·Kim, Young Mie·Lazer, David·Malhotra, Neil·Moehler, Devra·Pan, Jennifer·Thomas, Daniel Robert·Tromble, Rebekah·Rivera, Carlos Velasco·Wilkins, Arjun·Xiong, Beixian· de Jonge, Chad Kiewiet·Franco, Annie·Mason, Winter·Stroud, Natalie Jomini·Tucker, Joshua A.
Credibility Rating
5/5
Gold(5)Gold standard. Rigorous peer review, high editorial standards, and strong institutional reputation.
Rating inherited from publication venue: Nature
A high-profile empirical study in Nature examining how social media recommendation algorithms amplify political content, relevant to AI safety discussions about value alignment, autonomy preservation, and the societal impacts of deployed ML systems.
Metadata
Importance: 62/100journal articleprimary source
Summary
This Nature paper empirically investigates how recommendation algorithms on major social media platforms differentially amplify political content, finding systematic biases in how content is surfaced to users. The study provides large-scale evidence that algorithmic curation can shape political information exposure in ways that may affect democratic discourse and user autonomy.
Key Points
- •Empirically measures algorithmic amplification of political content across multiple major social media platforms using large-scale data.
- •Finds that algorithms systematically favor certain political content over others, raising concerns about unintended influence on political discourse.
- •Highlights tension between platform engagement optimization and neutral information delivery to users.
- •Raises governance questions about transparency and accountability of recommendation systems in politically sensitive contexts.
- •Relevant to AI safety concerns about persuasive AI systems and the autonomy-preserving properties of deployed ML systems.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Preference Manipulation | Risk | 55.0 |
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Like-minded sources on Facebook are prevalent but not polarizing | Nature
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Like-minded sources on Facebook are prevalent but not polarizing
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Subjects
Communication
Politics
An Author Correction to this article was published on 01 November 2023
This article has been updated
Abstract
Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem 1 , 2 . Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.
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