ArticlePNAS nexus2024
Nudging recommendation algorithms increases news consumption and diversity on YouTube.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Political identity, algorithmic news exposure, and trust in digital media: the sequential mediating associations of motivated reasoning and misinformation susceptibility.Frontiers in psychology · 2026Article
- Ad-based social media interventions increase belief accuracy and generate pro-social opinions among non-news readers.PloS one · 2026Article
- Following news on social media boosts knowledge, belief accuracy and trust.Nature human behaviour · 2025Article
- Independently testing prosocial interventions: Methods and recommendations from 31 researchers.Annals of the New York Academy of Sciences · 2025Review
- Engagement, user satisfaction, and the amplification of divisive content on social media.PNAS nexus · 2025Article
- Short-term exposure to filter-bubble recommendation systems has limited polarization effects: Naturalistic experiments on YouTube.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Incentivizing news consumption on social media platforms using large language models and realistic bot accounts.PNAS nexus · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.