Correction: Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact
Bibliographic record
Abstract
minor error in the references section in the originally published version of the editorial by Eysenbach (J Med Internet Res 2011;13[4]:e123) on the relationship between citations and tweetations has been corrected; in addition, references being part of the dataset are no longer cited as “references”. The now corrected problem with the references was a formatting problem only and had no impact on the study findings. The originally published article stated correctly that all 55 articles published between issue 3/2009 and 2/2010 were included, but the cited references erroneously contained 12 additional references from issue 2/2009, which were not part of the analysis, for the reasons described in the article (sparse tweetations pre-issue 3/2009). In the corrected version we have not only removed these extra 12 references, but we also took the opportunity to move all other references of included articles into a new Multimedia Appendix 2, no longer citing them in the “References” section. We now refer to them in the paper by article ID (last 4-digits of the DOI), where we previously used in-text citations (Table 2 and Discussion). The original decision to cite them as references was made for the sake of convenience for our readers, to prevent them from having to look up the references in a separate file or by DOI. JMIR has no space limitations and prefers to cite references in the article rather than in an Appendix; for readers downloading a PDF file it is more convenient to have all references in a single file rather than having to download a separate Appendix. The decision to now move these references into a Multimedia Appendix was made after a reader pointed out that citing these articles may increase JMIR’s impact factor. Although none of the two peer-reviewers, both experts in scientometrics, were originally concerned about citing the included articles as references, and even though any potential additional impact factor points after the decimal point caused by the original editorial would have been neglible (after all, these articles are already highly cited: altogether, 638 times, according to Google Scholar), we wish to avoid any potential debate or uncertainty on what proportion of future JMIR impact factors were caused by this editorial, and have therefore decided to pre-emptively move these references into a separate file (Multimedia Appendix 2). The article correction was made on January 4, 2012, before submission to PubMed Central, Swets and other content aggregators and databases, and before indexing by Thomson Reuters. Having to remove references from a manuscript to preserve the validity of a journal-level impact metric is somewhat troubling, but if anything then this perhaps illustrates the limitations and tyranny of the impact factor, and why we should consider additional metrics.
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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 | 0.019 | 0.393 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.059 | 0.040 |
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".