Research Information System
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.33, no.2, pp.392-409, 2025 (SCI-Expanded)
Vaccine hesitancy is a significant public healthcare problem that is
threatening everyone worldwide. Vaccine hesitancy has become more
ingrained in Turkish society, mainly through social media.
Unfortunately, reflections of this hesitancy are preventable deaths or
permanent disabilities. Because of the uncontrolled spread of
misinformation and disinformation on social media, Türkiye is facing a
future health crisis. As a step towards preventing this crisis, our main
objective is to use the power of artificial intelligence techniques on
Turkish social media posts to detect antivaccine posts. Through this
study, it will be possible to raise awareness about the importance of
vaccines in Turkish society, strengthen Türkiye’s defense mechanism
against potential epidemics, and ensure that our society exchanges
information in a healthier digital environment. We collected and cleaned
a novel Turkish social media dataset, resulting in 3778 posts. Then, we
used a baseline machine learning method, logistic regression, popular
machine learning methods, support vector machines, and XGBoost to detect
antivaccine thoughts and misleading information from Turkish social
media posts. Further, we included transformers that changed the natural
language processing domain. Evaluations are conducted using a
multilingual BERT and two models specifically trained for recognizing
Turkish texts, such as BERTurk. Results showed that transformers can
separate Turkish social media posts with antivaccine beliefs from other
posts with a 75.9% Area Under the ROC curve rate.