Enhancing engagement beyond the conference walls: analysis of Twitter use at #ICPIC2019 infection prevention and control conference
Bibliographic record
Abstract
BACKGROUND: Social media may provide a tool, when coupled with a patient-included™ conference, to enhance the engagement among the general public. We describe authors and potential readers of Twitter content surrounding a patient-included™ scientific congress, the International Consortium for Prevention and Infection Control (ICPIC) 2019. METHODS: Retrospective observational analysis of Twitter users posting with the #ICPIC2019 hashtag during the conference. Tweet authors, overall followers, and active followers were categorized according to their Twitter biographies using unsupervised learning. Diversity of professional backgrounds of Tweet authors and their followers was explored. Network analysis explored connectedness between the reach of authors. RESULTS: In total, 1264 participants attended ICPIC 2019, of which 28 were patients. From September 7 to 16, 2019, we were able to categorize 235'620 (41%) followers linked to 474 (76%) authors. Among authors and followers, respectively 34% and 14% were healthcare workers, 11% and 15% were from industry representatives, 8% and 7% were academic researchers. On average, 23% (range 9-39%) followers belonged to the same categories as authors. Among all followers categorized, only 582/235 620 (0.25%) interacted with original messages, including healthcare workers (37%), global and public health (12%), academic research (11%) and those from industry (11%). Though the similarity between Tweet authors and followers was supported by network analysis, we also observed that non-healthcare workers (including patients) appeared to have more diverse followers. CONCLUSIONS: We observed the participation of numerous Tweet authors and followers from diverse professional backgrounds potentially supporting the benefit of including patients in conferences to reach a more general, non-specialized public.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".