Ten Years of Pathology on Twitter (X): Landscape and Evolution of Pathology on Twitter From 2012 to 2023
Bibliographic record
Abstract
CONTEXT.—: Social media is a powerful tool in pathology education and professional networking that connects pathologists and pathology trainees from around the world. Twitter (X) appears to be the most popular social media platform pathologists use to share pathology-related content and connect with other pathologists. Although there has been some published research on pathology-related activity on Twitter during short time frames, to date there has not been published research examining pathology-related Twitter activity in totality from its earliest days of activity to recently. OBJECTIVE.—: To comprehensively evaluate the use of pathology on Twitter (X) during the last 10 years. DESIGN.—: Pathology-related tweets were systematically scraped from Twitter from January 2012 to January 2023 using pathology hashtags as a surrogate measure for all pathology content on Twitter. COVID-related tweets were approximated by tweets containing the term "COVID." RESULTS.—: There were 591 812 unique pathology-related tweets identified during the time period, with #pathology being the most common hashtag used and #PathTwitter becoming more popular since 2020. There has been positive annual growth of pathology Twitter, with peaks in use during major pathology conferences. During the initial phases of the COVID-19 pandemic, a sustained increase in pathology tweets was observed. CONCLUSIONS.—: Pathology Twitter has grown during the last 10 years and has become increasingly popular for pathology education and networking. With the changing landscape of social media platforms, this study provides an understanding of how pathology medical education and professional networking uses of social media happen and evolve over time.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".