Social Media Network Analysis of Academic Urologists’ Interaction Within Twitter Microblogging Environment
Notice bibliographique
Résumé
ObjectiveTo characterize academic urology Twitter presence and interaction by subspecialty designation.MethodsUsing Twitter application programming interface of available data, 94000 specific tweets were extracted for the analysis through the Twitter Developer Program. Academic urologists were defined based on American Urological Association (AUA) residency program registration of 143 residency programs, with a total of 2377 faculty. Two of 3-factor verification (name, location, specialty) of faculty Twitter account was used. Additional faculty information including sex, program location, and subspecialty were manually recorded. All elements of microblogging were captured through Anaconda Navigator. Analyzed tweets were further evaluated using natural language processing for sentiment association, mentions, and quote tweeted and retweeted. Network analysis based on interactions of academic urologist within specialty for given topic were analyzed using D3 in JavaScript. Analysis was performed in Python and R.ResultsWe identified 143 residency programs with a total of 2377 faculty (1975 men and 402 women). Among all faculty, 945 (39.7%) had registered Twitter accounts, with the majority being men (759 [80.40%] versus 185 [19.60%]). Although there were more male academic urologists across programs, women within academic urology were more likely to have a registered Twitter account overall (46% versus 38.5%) compared with men. When assessing registered accounts by sex, there was a peak for male faculty in 2014 (10.05% of all accounts registered) and peak for female faculty in 2015 (2.65%). There was no notable change in faculty account registration during COVID-19 (2019–2020). In 2022, oncology represented the highest total number of registered Twitter users (225), with the highest number of total tweets (24622), followers (138541), and tweets per user per day (0.32). However, andrology (50%) and reconstruction (51.3%) were 2 of the highest proportionally represented subspecialties within academic urology. Within the context of conversation surrounding a specified topic (#aua21), female pelvic medicine and reconstructive surgery (FPMRS) and endourology demonstrated the total highest number of intersubspecialty conversations.ConclusionsThere is a steady increase in Twitter representation among academic urologists, largely unaffected by COVID-19. While urologic oncology represents the largest group, andrology and reconstructive urology represent the highest proportion of their respective subspecialties. Interaction analysis highlights the variant interaction among subspecialties based on topic, with strong direct ties between endourology, FPMRS, and oncology.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».