Identifying science in the news: An assessment of the precision and recall of Altmetric.com news mention data
Bibliographic record
Abstract
The company Altmetric is often used to collect mentions of research in online news stories, yet there have been concerns about the quality of this data. This study investigates these concerns. Using a manual content analysis of 400 news stories as a comparison method, we analyzed the precision and recall with which Altmetric identified mentions of research in 8 news outlets. We also used logistic regression to identify the characteristics of research mentions that influence their likelihood of being successfully identified. We find that, for a predefined set of outlets, Altmetric’s news mention data were relatively accurate (F-score = 0.80), with very high precision (0.95) and acceptable recall (0.70), although recall is below 0.50 for some news outlets. Altmetric is more likely to successfully identify mentions of research that include a hyperlink to the research item, an author name, and/or the title of a publication venue. This data source appears to be less reliable for mentions of research that provide little or no bibliometric information, as well as for identifying mentions of scholarly monographs, conference presentations, dissertations, and non-English research articles. Our findings suggest that, with caveats, scholars can use Altmetric news mention data as a relatively reliable source to identify research mentions across a range of outlets with high precision and acceptable recall, offering scholars the potential to conserve resources during data collection. Our study does not, however, offer an assessment of completeness or accuracy of Altmetric news data overall.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.009 |
| 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; both teacher heads agree on what is shown here.
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".