ArticleScience advances2026
The Media Bias Detector: A framework for annotating and analyzing the news.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
News organizations introduce bias into their coverage via the choices they make about which topics to cover (or ignore) and how to frame the issues they do decide to cover. Here, we introduce the Media Bias Detector, a scalable computational framework that integrates large language models (LLMs) with near-real-time news scraping to extract structured annotations, including political lean, tone, topics, article type, and major events, across hundreds of articles per day. We quantify these dimensions of coverage at the sentence level, the article level, and the publisher level, expanding the ways in which researchers can analyze selection and framing bias in the modern news landscape. We also release an interactive web platform for convenient exploration of these data and an accompanying dataset covering more than 140,000 articles published in 2024 by 10 prominent publishers. Last, we present some results derived from this dataset that illustrate how the MBD can uncover correlates of bias in news coverage.
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.