ArticleInternational journal of environmental research and public health2021
Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy.Computers in biology and medicine · 2021Pooled it
- The Impact of Stay-At-Home Mandates on Uncertainty and Sentiments: Quasi-Experimental Study.Journal of medical Internet research · 2025Article
- Article
- Identification, analysis and prediction of valid and false information related to vaccines from Romanian tweets.Frontiers in public health · 2024Article
- Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images.Frontiers in public health · 2024Article
- COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.International journal of environmental research and public health · 2023Article
- ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning.PeerJ. Computer science · 2023Article
- Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations.Human vaccines & immunotherapeutics · 2022Article
- Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling.Journal of medical Internet research · 2022Article
- A review on Natural Language Processing Models for COVID-19 research.Healthcare analytics (New York, N.Y.) · 2022Review
- COVID-19 vaccine hesitancy: a social media analysis using deep learning.Annals of operations research · 2022Article
- Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic.Health and technology · 2022Article
- COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter.Vaccines · 2021Article
- Development and multicenter validation of chest X-ray radiography interpretations based on natural language processing.Communications medicine · 2021Article
- Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets.Digital healthArticle
- Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.