Measuring Twitter activity of arXiv e-prints and published papers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.067 | 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 it