Harnessing Twitter to empower scientific engagement and communication: The ISTH 2020 virtual congress experience
Bibliographic record
Abstract
As a result of the coronavirus disease 2019 pandemic, the International Society on Thrombosis and Haemostasis (ISTH), like many societies around the world, canceled their in-person hematology congress planned for Milan, Italy, in July 2020. As a result, the first virtual ISTH congress in the organisation’s 51-year history was delivered, inviting free registration from across the globe. As part of the social media support, marketing, and scientific dissemination efforts for the virtual congress, the ISTH assembled a group of official Twitter Ambassadors, which represented the broad and diverse ISTH community. Ambassadors were tasked to tweet daily throughout the congress and to share their commentary on the hematology research being presented with the “#ISTH2020” hashtag. Ambassadors were also supported by Twitter activities from the two official ISTH-affiliated journals: the Journal of Thrombosis and Haemostasis (JTH) and Research and Practice in Thrombosis and Haemostasis (RPTH). In this forum and through the Twitter ambassadors’ lens, we present the Twitter Ambassadors’ experience, reflect on the impact of social media on the ISTH 2020 congress, and share this experience with the wider scientific community. Specifically, we report on the role of Twitter communication for virtual meetings, discuss the pros and cons of the virtual congress, and offer Twitter-related recommendations for future virtual or blended congresses. We conclude that the ISTH Twitter Ambassador program broadened social media engagement and offers a novel route to improve social connectivity in the virtual research congress setting.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".