Bibliographic record
Abstract
In the recent political climate, the topic of news quality has drawn attention both from the public and the academic communities. The growing distrust of traditional news media makes it harder to find a common base of accepted truth. In this work, we design and build MediaRank (www.media-rank.com), a fully automated system to rank over 50,000 online news sources around the world. MediaRank collects and analyzes one million news webpages and two million related tweets everyday. We base our algorithmic analysis on four properties journalists have established to be associated with reporting quality: peer reputation, reporting bias / breadth, bottomline financial pressure, and popularity. Our major contributions of this paper include: (i) Open, interpretable quality rankings for over 50,000 of the world's major news sources. Our rankings are validated against 35 published news rankings, including French, German, Russian, and Spanish language sources. MediaRank scores correlate positively with 34 of 35 of these expert rankings. (ii) New computational methods for measuring influence and bottomline pressure. To the best of our knowledge, we are the first to study the large-scale news reporting citation graph in-depth. We also propose new ways to measure the aggressiveness of advertisements and identify social bots, establishing a connection between both types of bad behavior. (iii) Analyzing the effect of media source bias and significance. We prove that news sources cite others despite different political views in accord with quality measures. However, in four English-speaking countries (US, UK, Canada, and Australia), the highest ranking sources all disproportionately favor left-wing parties, even when the majority of news sources exhibited conservative slants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".