Evaluating the Factors Affecting Scholarly Communication of Journal Articles on Social and News Media: An Altmetric Study
Bibliographic record
Abstract
Different factors influence the altmetric attentions for scholarly articles on social media. This study aimed to evaluate the effecting factors on altmetric coverage of journals in non-English-speaking countries. Using a total population sampling technique, we included all Iranian and Turkish journals published from 2016 to 2019. Altmetric data were collected from altmetric.com using an application programming interface, for the coverage of mentions aggregated by the journal articles on Twitter, Facebook, and news media. The correlations between the languages, field of study, Google PageRank (GPR) score, and availability of a ‘share button’ with mentions were calculated using non-parametric tests. Among all articles, 2,378 articles were scholarly communicated on social media, and there were 7,191 mentions in the evaluated platforms. The scholarly publication of Iran and Turkey differed greatly concerning the subject matter. However, Twitter ranked first among the highly used alternative metrics for scholarly communication in both countries. The number of mentions for English journals was higher than for bilingual ones. Moreover, there was a positive correlation between the GPR and the coverage of mentions on Twitter and news 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 | 0.012 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".