World Gynecologic Oncology Day: the use of Twitter to raise awareness of gynecologic cancers
Bibliographic record
Abstract
BACKGROUND: Social media provides an opportunity for people to connect and form communities. This community architecture can help to disseminate health-related information in the form of an awareness campaign. The European Society of Gynaecological Oncology and the European Network of Gynaecological Cancer Advocacy Groups initiated a global campaign, World Gynecologic Oncology Day, on September 20, 2020. We studied and analyzed the impact and reach of this Twitter campaign. OBJECTIVE: This study aimed to assess the impact and reach of the 2020 World Gynecologic Oncology Day Twitter campaign. STUDY DESIGN: We analyzed gynecologic oncology-specific posts (tweets) between 12 am on September 17, 2020, to 11:59 pm on September 25, 2020 (Coordinated Universal Time), covering the days immediately before and after World Gynecological Oncology Day (September 20, 2020), using Tweepy. The European Network of Gynecological Cancer Advocacy Groups suggested hashtags (#GoForPurple, #WorldGODay, and #GoForCheckup) should be used for this social media campaign. We used these hashtags for our data (tweet) collection. RESULTS: A total of 382 Twitter accounts participated in this campaign and 662 tweets, including retweets, were reported. Of those, 22% of participants were healthcare professionals. A total of 164 unique hashtags were identified, and #WorldGODay was the most frequently used among the Twitter accounts. #VaginalCancer, #CervicalCancer, and #VulvarCancer were used in relation to the campaign. We identified 5 significant communities that contributed to raising awareness. CONCLUSION: Twitter campaigns should be designed around a single, short, easy-to-spell hashtag and coordinated with previously identified influential accounts using timed tweets. #WorldGOday hashtag was relevant, easy to spell, memorable, and the most effective hashtag used in this campaign.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".