Examining the Use of a Social Media Campaign to Increase Engagement for the American Heart Association 2017 Resuscitation Science Symposium
Bibliographic record
Abstract
Background The Resuscitation Science Symposium (Re SS ) is the dedicated international forum for resuscitation science at the American Heart Association's Scientific Sessions. In an attempt to increase curated content and social media presence during Re SS 2017, the Journal of the American Heart Association (JAHA) coordinated an inaugural social media campaign. Methods and Results Before Re SS , 8 resuscitation science professionals were recruited from a convenience sample of attendees at Re SS 2017. Each blogger was assigned to either a morning or an afternoon session, responsible for “live tweeting” with the associated hashtags #Re SS 17 and # AHA 17. Twitter analytics from the 8 bloggers were collected from November 10 to 13, 2017. The primary outcome was Twitter impressions. Secondary outcomes included Twitter engagement and Twitter engagement rate. In total, 8 bloggers (63% male) generated 591 tweets that garnered 261 050 impressions, 8013 engagements, 928 retweets, 1653 likes, 292 hashtag clicks, and a median engagement rate of 2.4%. Total engagement, likes, and hashtag clicks were highest on day 2; total impressions were highest on day 3, and retweets were highest on day 4. Total impressions were highly correlated with the total number of tweets ( r =0.87; P =0.005) and baseline number of Twitter followers for each blogger ( r =0.78; P =0.02). Conclusion In this inaugural social media campaign for the 2017 American Heart Association Re SS , the degree of online engagement with this content by end users was quite good when evaluated by social media standards. Benchmarks for end‐user interactions in the scientific community are undefined and will require further study.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".