1391. "Going Viral for Good: The Global Impact of #IDTwitter in the Infectious Diseases Twitter Community"
Notice bibliographique
Résumé
Abstract Background Twitter has become an invaluable resource for gaining insights into crucial developments in global healthcare communication. The hashtags (#) can be used to categorize tweets and gather conversations on a specific topic or to target a particular audience. Although the prevailing knowledge fund underlines the potential of digital networks to essentially influence the management of infectious diseases (ID), there has been no comprehensive analysis on the user information in the ID Twitter community, nor of the influence this hashtag has generated. The objective of our study was to evaluate the demographic data of #IDTwitter users, discern the most influential members and popular narratives in this realm, and ascertain the impact of the hashtag. Methods Using data from 28th June 2019 and 28th March 2023, an extensive analysis was conducted using the Symplur Signals research analytics tool. The analysis focused on the cumulative number of tweets, impressions, and unique users who shared tweets containing the hashtag #IDTwitter, with users categorized into specific healthcare stakeholder groups. The primary outcome measures were outreach and awareness measured by the number of tweets and impressions. Results The study observed the trends of #IDTwitter over a period of 45 months and found 441,650 tweets were shared by 92,734 users that generated a total of 1,833,037,732 impressions (views). Top five co-occurring hashtags were #IDtwitter, #MedTwitter, #MedEd, #COVID19, #TwitteRx. The top five countries reporting the greatest number of users of this hashtag were The United States of America (48326), Canada (4803), Mexico (3413), India (2759), Australia (2658). Various healthcare stakeholders’ categories were identified and three largest groups of contributors were Doctors (14.55%), Healthcare Providers (7.54%) and Researcher/Academic (3.70%). The top three influencers of this hashtag include two clinical pharmacists and one organization account. Country-wise distribution of Users of #IDTwitter The image shows the geographical distribution of the users who posted tweets containing #IDTwitter were shared (based on the locations at which the posting accounts were registered). Twitter is used worldwide for conversations regarding communicable diseases and their prevention, not only among the general public but also among students and professionals via online chat discussions and virtual rounds. Stakeholders of #IDTwitter Accounting for the percentage distribution of #IDTwitter-posting users in various healthcare stakeholders categories (data derived from Symplur Signals, with the classification being based on information provided in the Twitter biographies of the users- https://help.symplur.com/en/articles/103684-healthcare-stakeholder-segmentation). Twitter has become a quintessential tool for connecting people worldwide, and we can leverage this platform to our advantage by paying close attention to the topic and content of hashtag exchanges to combat misinformation related to matters like antibiotic usage or any epidemic infection on social media platforms. Top Co-occurring Hashtags The hashtags that generally appear alongside of #IDTwitter could provide an insight regarding the popular discussions involving #IDTwitter. Conclusion Our findings indicate that there is considerable interest in using #IDTwitter to promote relevant content and engage a geographically diverse audience. It underscores the vitality of professional voices in combating misinformation and we could definitely leverage this 'viral' hashtag for our advantage. Disclosures All Authors: No reported disclosures
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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,003 |
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 ».