2022
DOI: 10.1016/j.chb.2022.107320
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The popularity of contradictory information about COVID-19 vaccine on social media in China

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Cited by 13 publications
(9 citation statements)
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“…The relatively high level of information heterogeneity and polarization in social media renders it challenging for online users’ vaccine decision-making with contradictory information (P. Wang, Fan, & Li, 2020 ). In order to alleviate this situation, Wang and Zhou (2022) suggested that public health departments were expected to release relatively coherent information promptly to avert information vacuums, cognitive deficits, and narrow biases. Rieger (2020) suggested emphasizing the altruistic concept of protecting others while convincing people to get the COVID-19 vaccine.…”
Section: Relevant Researchmentioning
confidence: 99%
“…The relatively high level of information heterogeneity and polarization in social media renders it challenging for online users’ vaccine decision-making with contradictory information (P. Wang, Fan, & Li, 2020 ). In order to alleviate this situation, Wang and Zhou (2022) suggested that public health departments were expected to release relatively coherent information promptly to avert information vacuums, cognitive deficits, and narrow biases. Rieger (2020) suggested emphasizing the altruistic concept of protecting others while convincing people to get the COVID-19 vaccine.…”
Section: Relevant Researchmentioning
confidence: 99%
“…Classical machine learning models mainly included naïve Bayes, support vector machine, random forest, decision tree, and logistic regression [ 60 , 61 , 67 , 70 , 76 ]. Additional machine learning techniques for polarity classification were Microsoft Azure cognitive services, Amazon Web Services (AWS), and Baidu’s AipNLP [ 59 , 64 , 75 ]. Deep learning techniques mainly used convolutional neural networks, recurrent neural networks, bidirectional long short-term memory (LSTM), and Bidirectional Encoder Representations from Transformers [ 63 , 65 , 66 , 68 , 71 , 77 ].…”
Section: Resultsmentioning
confidence: 99%
“…The degree of vaccine hesitancy in young women is significantly higher than that in young men, and the difference between the age groups in young men is higher than that in young women ( 10 ). In China, non-medical personnel, adults who have been vaccinated against influenza and old people have a low degree of hesitation about vaccination ( 11 ). Compared with medical students, most non-medical students are hesitant to receive COVID-19 vaccine ( 12 ).…”
Section: Related Workmentioning
confidence: 99%
“…Health care providers and the medical community are encouraged to adopt a multi-level strategy and effectively “Tweet up” to counter the growing threat posed by vaccine misinformation and hesitancy ( 26 ). Suggestions for adjusting the organizational strategy of contradictory information to control its popularity from different dimensions, such as poster's influence, activity and identity, tweets' topic, sentiment, readability are proposed, to reduce vaccine hesitancy ( 11 ).…”
Section: Related Workmentioning
confidence: 99%