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Identifying Anti-Vaccination Tweets in Arabic Language Utilizing NLP, ML, and DL Approaches

Twitter has had a prominent role during the Covid-19 pandemic in discussing public healthcare-related events, including vaccine efficacy. In this context, the sentiments of individuals about the vaccine have varied between supporters of vaccination programs, and opponents who advocate others not to vaccinate. Being able to identify anti-vaxxers through their tweets could enable public health organizations to make wise decisions to enhance vaccine acceptance, by allocating customized awareness campaigns to individuals to reduce anti-vaccination sentiments among different groups. This study aims to provide a smart classification model that can identify the sentiments of individuals in the Middle East region toward Covid-19 vaccinations through the Arabic tweets they publish on their own Twitter accounts, exploiting Natural Language Processing, Machine Learning, and Deep Learning-based models. Nine well-known Machine Learning algorithms and four Deep Learning algorithms were investigated with various feature extraction techniques (TF, TF-IDF, and Glove). The results show that the performance of the TF model outperforms other models in the Multinomial Naïve Bayes with an accuracy of 82.14%. On the other hand, the performance of the TF-IDF model outperforms other models in the Support Vector Machine with an accuracy of 81.81%. Finally, the Glove word embedding model outperforms other models in the LSTM with an accuracy of 81.67%.