Vaccine hesitancy in Türkiye: A natural language processing study on social media

Basit Öğe Kaydı
dc.authorid0000-0002-8150-4053
dc.contributor.authorSari, Semih
dc.contributor.authorBayram, Ulya
dc.date.accessioned2026-02-03T12:03:22Z
dc.date.available2026-02-03T12:03:22Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractVaccine 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, T & uuml;rkiye 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 T & uuml;rkiye'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.
dc.identifier.doi10.55730/1300-0632.4133
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105007664234
dc.identifier.scopusqualityQ2
dc.identifier.trdizinid1333473
dc.identifier.urihttps://doi.org/10.55730/1300-0632.4133
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1333473
dc.identifier.urihttps://hdl.handle.net/20.500.12428/35058
dc.identifier.volume33
dc.identifier.wosWOS:001521286100010
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectNatural language processing
dc.subjectmachine learning
dc.subjecttransformers
dc.subjectvaccine hesitancy
dc.subjectTurkish texts
dc.subjectsocial media
dc.titleVaccine hesitancy in Türkiye: A natural language processing study on social media
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu