Factors Influencing Engagement Rate Of A Daily Twitter Campaign To Disseminate Exercise Oncology Literature
Bibliographic record
Abstract
Twitter is a potential medium for health researchers and practitioners to disseminate research findings to colleagues and the general public. The Clinical Exercise Physiology Lab (CEPL; Vancouver, Canada) led a scientific Twitter campaign (#365Papers) in 2018, involving tweeting daily about one peer-reviewed publication related to cancer/exercise. Features of this campaign included Throwback Thursdays (selected article published prior to 2018) and guest tweeters (selected article chosen by other exercise oncology researchers). PURPOSE: To report on the impact to-date of the #365Papers campaign and explore how engagement rate differed based on publication features (type of research (i.e., systematic review, randomized controlled trial), 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) and summarized using descriptive statistics. Publication information was extracted by screening titles and abstracts. Each publication’s Altmetric Attention Score was obtained using the Altmetric Bookmarklet (Digital Science, Holtzbrinck Publishing Group, 2020: Stuttgart, Germany). RESULTS: As of October 2020, the #365Papers Twitter campaign received a total of 688,117 impressions and 22,124 engagements (median engagement rate = 3.2%), with the majority of engagement from URL clicks (n = 8279; 37%). 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). CONCLUSIONS: A daily scientific Twitter campaign can promote engagement around new scientific publications, especially when prominent figures in the research field are incorporated in the campaign process.
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.
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 | 0.020 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".