The Impact of #365Papers: A Daily Scientific Twitter Campaign to Disseminate Exercise Oncology Literature
Bibliographic record
Abstract
Purpose: Many health researchers and practitioners use Twitter to stimulate scientific dialogue and collaboration among peers, as well as the general public. In 2018, the Clinical Exercise Physiology Lab (CEPL) undertook a year-long scientific Twitter campaign (#365Papers) where one peer-reviewed publication related to cancer and exercise/physical activity was tweeted per day. Features of this campaign included Throwback Thursdays (selected article published before 2018) and guest tweeters (article chosen by other exercise oncology researchers). We report on the impact of the #365Papers campaign based on Twitter Analytics data (i.e., engagement rate). We also explore how engagement rate differed depending on publication features (e.g., type of research, journal impact factor, Altmetric Attention Score) and campaign features (i.e., Throwback Thursdays, guest tweeters). Methods: Campaign data were obtained from Twitter Analytics (Twitter, 2020: San Francisco, USA). Publication information (i.e., type of research, journal) was extracted by screening titles and abstracts, while each publication’s Altmetric Attention Score was obtained using the Altmetric Bookmarklet (Digital Science, Holtzbrinck Publishing Group, 2020: Stuttgart, Germany). Twitter Analytics data were summarized using descriptive statistics. Differences in engagement rate were analyzed based on research type (e.g., randomized controlled trial), journal impact factor, Altmetric Attention Score, and if the publication was posted as part of a Throwback Thursday or by a guest tweeter. Results: The #365Papers Twitter campaign received a total of 688,117 impressions and 22,124 engagements, with a median engagement rate of 3.2% and the majority of engagement from URL clicks (n=8279; 37%). The mean monthly increase in CEPL Twitter account followers was 48 (±18). Engagement rate did not differ based on type of research (p=0.53), journal impact factor (r=-0.06; p=0.27), Altmetric Attention Score (r=0.01; p=0.80), nor if the tweet was part of a Throwback Thursday (p=0.97). However, guest tweets had significantly higher engagement rates versus non-guest tweets (median: 3.6% vs. 3.1%; p=0.01). Conclusion: Our findings suggest the potential of a daily scientific Twitter campaign to stimulate peer and public engagement and dialogue around new scientific publications, especially when prominent figures in the research field are incorporated into the campaign process.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".