The impact of Twitter/X promotion on visibility of research articles: Results of the #TweetTheJournal study
Bibliographic record
Abstract
Aim: Social media (SoMe) are emerging as important tools for research dissemination. Twitter/X promotion has been shown to increase citation rates in well-established journals. We aimed to test the effect of a SoMe promotion strategy on the Mendeley reader counts, the Altmetric Attention Score and the number of citations in an upcoming open-access journal. Methods: The #TweetTheJournal study is a randomized, controlled study. Articles published in seven subsequent issues of the International Journal of Cardiology Heart & Vasculature (April 2021-April 2022) were randomized to a Twitter/X promotion arm (articles were posted four times) and to a control arm (without active posting). Articles with accompanied editorials were excluded. Primary endpoint of the study was Mendeley reader count, secondary endpoints were Altmetric Attention Score and number of citations. Follow-up was one year. Results: SoMe promotion of articles showed no statistically significant difference in Mendeley reader counts or number of citations at one year follow up. SoMe promotion resulted in a statistically significant higher Altmetric Attention Score in the intervention compared to the control group (RR 1.604, 95 % CI 1.024-2.511, p = 0.039). In the overall group, Altmetric Attention Score showed a correlation with Mendeley reader counts (Spearman's ρ = 0.202, p = 0.010) and Mendeley reader counts correlated significantly with number of citations (Spearman's ρ = 0.372, p < 0.001). Conclusion: A dedicated SoMe promotion strategy did not result in statistically significant differences in early impact indicators as the Mendeley reader count in a upcoming journal, but increased the Altmetric Attention Score.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".