Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation.
Trop Med Infect Dis
; 7(10)2022 Sep 22.
Article
in English
| MEDLINE | ID: covidwho-2043966
ABSTRACT
This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Randomized controlled trials
Topics:
Vaccines
Language:
English
Year:
2022
Document Type:
Article
Affiliation country:
Tropicalmed7100256
Similar
MEDLINE
...
LILACS
LIS