Best practices for source-based research on misinformation and news trustworthiness using NewsGuard
Bibliographic record
Abstract
Researchers need reliable and valid tools to identify cases of untrustworthy information when studying the spread of misinformation on digital platforms. A common approach is to assess the trustworthiness of sources rather than individual pieces of content. One of the most widely used and comprehensive databases for source trustworthiness ratings is provided by NewsGuard. Since creating the database in 2019, NewsGuard has continually added new sources and reassessed existing ones. While NewsGuard initially focused only on the US, the database has expanded to include sources from other countries. In addition to trustworthiness ratings, the NewsGuard database contains various contextual assessments of the sources, which are less often used in contemporary research on misinformation. In this work, we provide an analysis of the content of the NewsGuard database, focusing on the temporal stability and completeness of its ratings across countries, as well as the usefulness of information on political orientation and topics for misinformation studies. We find that trustworthiness ratings and source coverage have remained relatively stable since 2022, particularly for the US, France, Italy, Germany, and Canada, with US-based sources consistently scoring lower than those from other countries. Additional information on the political orientation and topics covered by sources is comprehensive and provides valuable assets for characterizing sources beyond trustworthiness. By evaluating the database over time and across countries, we identify potential pitfalls that compromise the validity of using NewsGuard as a tool for quantifying untrustworthy information, particularly if dichotomous "trustworthy"/"untrustworthy" labels are used. Lastly, we provide recommendations for digital media research on how to avoid these pitfalls and discuss appropriate use cases for the NewsGuard database and source-level approaches in general.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 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".