Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

Quyen G. To, Kien G. To, Huynh Van-Anh N., Nhung T. Q. Nguyen, Diep T. N. Ngo, Stephanie J. Alley, Anh N. Q. Tran, Anh N. P. Tran, Ngan T. T. Pham, Thanh X. Bui and Corneel Vandelanotte
Additional contact information
Quyen G. To: Physical Activity Research Group, Appleton Institute, Central Queensland University, Rockhampton, QLD 4701, Australia
Kien G. To: Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
Huynh Van-Anh N.: Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
Nhung T. Q. Nguyen: Trung Vuong Hospital, Ho Chi Minh City 700000, Vietnam
Diep T. N. Ngo: Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
Stephanie J. Alley: Physical Activity Research Group, Appleton Institute, Central Queensland University, Rockhampton, QLD 4701, Australia
Anh N. Q. Tran: Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
Anh N. P. Tran: Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
Ngan T. T. Pham: Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
Thanh X. Bui: Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
Corneel Vandelanotte: Physical Activity Research Group, Appleton Institute, Central Queensland University, Rockhampton, QLD 4701, Australia

IJERPH, 2021, vol. 18, issue 8, 1-9

Abstract: Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.

Keywords: deep learning; neural network; LSTM; BERT; transformer; stance analysis; vaccine (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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