ArticlePNAS nexus2024
Unveiling the hidden agenda: Biases in news reporting and consumption.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- A computational analysis of human rights discourse in news media using institutional theory.Frontiers in big data · 2026Article
- Ideology and polarization set the agenda on social media.Scientific reports · 2025Article
- The role of evidence-based medicine in the media: misinformation and the COVID-19 lesson.European respiratory review : an official journal of the European Respiratory Society · 2025Article
- Unveiling the drivers of active participation in social media discourse.Scientific reports · 2025Article
- Social media trust predicts lower COVID-19 vaccination rates and higher excess mortality over 2 years.PNAS nexus · 2023Article
- The Process of Urban Older People Surrendering Their Driver's License: An Analysis Using a Trajectory Equifinality Model.Sage open agingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recognizing the presence and impact of news outlets' biases on public discourse is a crucial challenge. Biased news significantly shapes how individuals perceive events, potentially jeopardizing public and individual wellbeing. In assessing news outlet reliability, the focus has predominantly centered on narrative bias, sidelining other biases such as selecting events favoring specific perspectives (selection bias). Leveraging machine learning techniques, we have compiled a six-year dataset of articles related to vaccines, categorizing them based on narrative and event types. Employing a Bayesian latent space model, we quantify both selection and narrative biases in news outlets. Results show third-party assessments align with narrative bias but struggle to identify selection bias accurately. Moreover, extreme and negative perspectives attract more attention, and consumption analysis unveils shared audiences among ideologically similar outlets, suggesting an echo chamber structure. Quantifying news outlets' selection bias is crucial for ensuring a comprehensive representation of global events in online debates.
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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.