  The most effective of the solutions against Covid-19 is the various vaccines
developed. Distrust of vaccines can hinder the rapid and effective use of this
remedy. One of the means of expressing the thoughts of society is social media.
Determining the time intervals during which anti-vaccination increases in
social media can help institutions determine the strategy to be used in
combating anti-vaccination. Recording and tracking every tweet entered with
human labor would be inefficient, so various automation solutions are needed.
In this study, The Bidirectional Encoder Representations from Transformers
(BERT) model, which is a deep learning-based natural language processing (NLP)
model, was used. In a dataset of 1506 tweets divided into four different
categories as news, irrelevant, anti-vaccine, and vaccine supporters, the model
was trained with a learning rate of 5e-6 for 25 epochs. To determine the
intervals in which anti-vaccine tweets are concentrated, the categories to
which 652840 tweets belong were determined by using the trained model. The
change of the determined categories overtime was visualized and the events that
could cause the change were determined. As a result of model training, in the
test dataset, the f-score of 0.81 and AUC values for different classes were
obtained as 0.99,0.91, 0.92, 0.92, respectively. In this model, unlike the
studies in the literature, an auxiliary system is designed that provides data
that institutions can use when determining their strategy by measuring and
visualizing the frequency of anti-vaccine tweets in a time interval, different
from detecting and censoring such tweets.
