Second-order citations in altmetrics: A case study analyzing the audiences of COVID-19 research in the news and on social media
Bibliographic record
Abstract
Abstract The potential to capture the societal impact of research has been a driving motivation for the use and development of altmetrics. Yet, to date, altmetrics have largely failed to deliver on this potential because the primary audience that cites research on social media has been shown to be academics themselves. In response, our study investigates an extension of traditional altmetric approaches that goes beyond capturing direct mentions of research on social media. Using research articles from the first months of the COVID-19 pandemic as a case study, we demonstrate the value of measuring “second-order citations,” or social media mentions of news coverage of research. We find that a sample of these citations, published by just five media outlets, were shared and engaged with on social media twice as much as the research articles themselves. Moreover, first-order and second-order citations circulated among Twitter accounts and Facebook accounts that were largely distinct from each other. The differences in audiences and engagement patterns found in this case study provide strong evidence that investigating these second-order citations can be an effective way of observing overlooked audiences who engage with research content on social media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.031 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".