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10.3390/ijerph18084069 http://scihub22266oqcxt.onion/10.3390/ijerph18084069 33921539!8069687!33921539     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33921539&cmd=llinks): Failed to open stream: HTTP request failed! 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Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic |pdf=|usr=}}{{33921539}} gab.com Text Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33921539 #FOAvip 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 naive 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. Twit Text FOAVip Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33921539 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33921539 #FOAvip English Wikipedia <ref>{{Cite pmid|33921539}}</ref> Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic #MMPMID33921539 To QG; To KG; Huynh VN; Nguyen NTQ; Ngo DTN; Alley SJ; Tran ANQ; Tran ANP; Pham NTT; Bui TX; Vandelanotte C Int J Environ Res Public Health 2021[Apr]; 18 (8): ä PMID33921539show ga 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 naive 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. |*COVID-19[MESH] |*Social Media[MESH] |Bayes Theorem[MESH] |Humans[MESH] |Machine Learning[MESH] |Pandemics[MESH]Applying Machine Learning to Identify Anti-Vaccination Tweets during t(epmc).pdf DeepDyve Pubget Overpricing