Guess et al., Science Advances (2023)

paper

Science(peer-reviewed)·science.org/doi/10.1126/sciadv.abq7422

Credibility Rating

5/5

Gold(5)

Gold standard. Rigorous peer review, high editorial standards, and strong institutional reputation.

Rating inherited from publication venue: Science

Relevant to AI safety discussions around algorithmic amplification, platform governance, and the societal effects of recommendation systems; questions whether design interventions in AI-driven feeds can meaningfully reduce harms like polarization.

Metadata

Importance: 52/100journal articleprimary source

Summary

This large-scale field experiment with Facebook users during the 2020 US election examined whether algorithmic ranking of news feeds increases political polarization and misinformation exposure. The study found that replacing algorithmic feeds with chronological feeds reduced exposure to ideologically cross-cutting content and misinformation, but had minimal effects on political attitudes and polarization outcomes.

Key Points

•Randomized controlled trial with ~23,000 US Facebook users during the 2020 election tested effects of chronological vs. algorithmic news feeds.
•Algorithmic ranking increased exposure to politically congruent content and misinformation compared to chronological feeds.
•Despite reducing problematic content exposure, chronological feeds did not meaningfully reduce affective or ideological polarization.
•Results suggest that filter bubbles and algorithmic amplification of misinformation alone do not drive political polarization outcomes.
•Findings challenge simple narratives that platform algorithms are primary drivers of societal polarization.

Cited by 1 page

Page	Type	Quality
AI-Accelerated Reality Fragmentation	Risk	28.0

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