Measuring Twitter activity of arXiv e-prints and published papers
Bibliographic record
Abstract
<em>Presentation accepted at #altmetrics14 #WebSci1</em>4<br><strong><br>Introduction</strong>. In the fields of Physics, Mathematics and Computer science, depositing preprints or e-prints on arXiv is part of the publication cycle, as it provides access to papers, limits publication delays and establishes priority claims (Brooks, 2009). Between 1995 and 2011, about two-thirds of all arXiv e-prints could be matched to a journal article indexed in the Web of Science (Larivière et al., 2014). However, very little is known about the dissemination of these two versions across social media. The microblogging service Twitter has been identified as a tool used by academics and the general public to distribute, among other things, links to scholarly documents (Thelwall et al., 2013). Preliminary studies have demonstrated that the majority of tweets related to a scientific document appear shortly after its online availability, reaching a peak of activity much faster than citations and downloads (Eysenbach, 2011; Shuai, Pepe & Bollen, 2012). This suggests that an important share of tweets to papers in Physics, Computer science and Mathematics are likely to be made to the arXiv rather than the published version of the paper. This study investigates the challenges of measuring Twitter activity to the journal of record and arXiv versions of scientific documents.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".