Detection of COVID-19 Anti-Vaccination from Twitter Data Using Deep Learning and Feature Selection Approaches

Year 2024, Volume: 3 Issue: 2, 116 - 133, 12.06.2024

Serdar Ertem , Erdal Özbay

https://doi.org/10.62520/fujece.1443753

Abstract

The COVID-19 pandemic has evolved into a crisis significantly impacting health, the economy, and social life worldwide. During this crisis, anti-vaccination sentiment poses a considerable obstacle to controlling the epidemic and the effectiveness of vaccination campaigns. This study aimed to detect COVID-19 anti-vaccination sentiment from Twitter data using a combination of deep learning and feature selection approaches. The proposed method integrates a deep learning model with feature selection techniques to identify anti-vaccination sentiment by pinpointing important features in text data. Hybrid TF-IDF and N-gram methods were utilized for feature extraction, followed by Chi-square feature selection. The dataset comprises Twitter text data and two labels. The Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the labels. Long Short-Term Memory (LSTM), a deep learning architecture, was employed for the classification process. The experimental results, obtained by leveraging the proposed feature extraction, feature selection, and LSTM methods, achieved the highest accuracy value of 99.23%. These findings demonstrate the proposed methods' success in effectively detecting COVID-19 anti-vaccination sentiment in text data. The study's results can offer valuable insights for developing health policies and public information strategies, presenting a new and powerful tool for detecting anti-vaccine sentiment in planning vaccination campaigns and public health interventions.

Keywords

COVID-19 anti-vaccine, Deep learning, Feature selection, LSTM, Text Classification

Ethical Statement

“There is no need for an ethics committee approval in the prepared article” “There is no conflict of interest with any person/institution in the prepared article”

Supporting Institution

Fırat University (FUBAP)

Project Number

MF.23.37.

Thanks

This study was funded by Fırat University (FUBAP) with the scientific research project number MF.23.37.

Derin Öğrenme ve Özellik Seçimi Yaklaşımları Kullanılarak Twitter Verilerinden COVID-19 Aşı Karşıtlığı Tespiti

Abstract

COVID-19 pandemisi, dünya genelinde sağlık, ekonomi ve toplumsal yaşamı derinden etkileyen bir krize dönüşmüştür. Bu kriz sırasında aşı karşıtlığı, salgının kontrolü ve aşılama kampanyalarının etkinliği açısından önemli bir engel oluşturmaktadır. Bu çalışmada, derin öğrenme ve özellik seçimi yaklaşımlarının birleşimi kullanılarak COVID-19 aşı karşıtlığının twitter verilerinden tespiti amaçlanmıştır. Önerilen yöntem, derin öğrenme modeli ile özellik seçimi tekniklerinin entegrasyonunu içermekte ve metin verilerindeki önemli özellikleri belirleyerek aşı karşıtlığını tanımlamaktadır. Özellik çıkarımı için TF-IDF ve N-gram yöntemleri hibrit kullanılmış, ardından Ki-kare özellik seçimi gerçekleştirilmiştir. Veri seti twitter text verilerinden ve iki etiketten oluşmaktadır. Etiketlerin dengelenmesi için Sentetik Azınlık Aşırı Örnekleme Tekniği (SAAÖT) yöntemi uygulanmıştır. Sınıflandırma işlemi için derin öğrenme mimarilerinden Uzun Kısa-Süreli Bellek (UKSB) kullanılmıştır. Önerilen özellik çıkarımı, özellik seçimi ve UKSB yöntemlerinin birlikte kullanılmasıyla elde edilen deneysel sonuçlara göre %99.23 ile en yüksek doğruluk değerine ulaşılmıştır. Bu sonuçlar, COVID-19 aşı karşıtlığının metin verileri üzerinde etkili bir şekilde tespit edilmesi için önerilen yöntemlerin başarılı bir şekilde kullanılabileceğini göstermektedir. Çalışmanın sonuçları, sağlık politikalarının ve kamuoyu bilgilendirme stratejilerinin geliştirilmesine yönelik değerli bilgiler sunabilmektedir. Bu bakımdan, aşılama kampanyaları ve halk sağlığı müdahaleleri planlanırken, aşı karşıtlığı belirlemede yeni ve güçlü bir araç geliştirilmiştir.

Keywords

COVID-19 aşı-karşıtlığı, Derin öğrenme, Özellik seçimi, UKSB, Metin sınıflandırma

Project Number

MF.23.37.

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