Detection and Characterization of Web-Based Pediatric COVID-19 Vaccine Discussions and Racial and Ethnic Minority Topics: Retrospective Analysis of Twitter Data
Notice bibliographique
Résumé
Background: Despite pediatric populations representing a smaller proportion of COVID-19 cases and having a less severe prognosis, those belonging to racial and ethnic minority groups are at an increased risk of developing more severe COVID-19-related outcomes. Vaccine coverage is crucial to pandemic mitigation efforts, yet since the start of the COVID-19 pandemic, vaccine hesitancy has increased and routine pediatric immunizations have decreased. Limited research exists on how vaccine hesitancy may contribute to low pediatric COVID-19 vaccine uptake among racial and ethnic minority populations. Objective: This study aimed to characterize COVID-19 vaccine-related discussion and sentiment among Twitter users, particularly among racial and ethnic minority users. Methods: We used the Twitter application programming interface to collect tweets and replies. Tweets were selected by filtering for keywords associated with COVID-19 vaccines and pediatric-related terms. From this corpus of tweets, we used the Biterm Topic Model to output topics and examined the top 200 retweeted tweets that were coded for pediatric COVID-19 vaccine relevance. Relevant tweets were analyzed using an inductive coding approach to characterize pediatric COVID-19 vaccine-related themes. Replies to relevant tweets were collected and coded. User metadata were assessed for self-reporting of race or ethnic group affiliation and verified account status. Results: A total of 863,007 tweets were collected from October 2020 to October 2021. After outputting Biterm Topic Model topics and reviewing the 200 most retweeted tweets, 208,666 tweets and 3905 replies were identified as being pediatric COVID-19 vaccine related. The majority (150,262/208,666, 72.01%) of tweets expressed vaccine-related concerns. Among tweets discussing vaccine confidence, user replies expressing agreement were significantly outweighed by those expressing disagreement (1016/3106, 32.71% vs 2090/3106, 67.29%; P<.001). The main themes identified in the Twitter interactions were conversations regarding vaccine-related concerns including adverse side effects, concerns that the vaccine is experimental or needs more testing and should not be tested on pediatric populations, the perception that the vaccine is unnecessary given the perceived low risk of pediatric infection, and conversations associated with vaccine-related confidence (ie, the vaccine is protective). Among signal tweets and replies, we identified 418 users who self-identified as a racial minority individual and 40 who self-identified as an ethnic minority individual. Among the subcodes identified in this study, the vaccine being protective was the most discussed topic by racial and ethnic minority groups (305/444, 68.7%). Conclusions: Vaccine-related concerns can have negative consequences on vaccine uptake and participation in vaccine-related clinical trials. This can impact the uptake and development of safe and effective vaccines, especially among racial and ethnic minority populations.
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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 | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 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) | 0,002 | 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 ».