Exploring and Monitoring the Reasons for Hesitation with COVID-19 Vaccine Based on Social-Platform Text and Classification Algorithms

Liu, Jingfang; Lu, Shuangjinhua; Lu, Caiying

doi:10.3390/healthcare9101353

Cited by 11 publications

(9 citation statements)

References 52 publications

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“…1 not only subdivides the hesitant and resistant types into four types but also structures them according to the two dimensions of agency and health capital. When compared to a typology of 12 COVID-19 vaccine hesitancy reasons ( Liu et al, 2021 ), the four ideal types proposed in this article are based on more general underlying mechanisms, each of which can be assumed to be driven by one or more of these 12 reasons. Underlying diseases, the possibility of adverse reactions, pregnancy and lactation, fear of needles, and allergies, for example, might to some degree contribute to the mechanism of personal fear.…”

Section: Resultsmentioning

confidence: 99%

COVID-19 Vaccine Hesitancy in Denmark and Russia: A qualitative typology at the nexus of agency and health capital

Schneider‐Kamp

2022

SSM - Qualitative Research in Health

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Section: Resultsmentioning

confidence: 99%

COVID-19 Vaccine Hesitancy in Denmark and Russia: A qualitative typology at the nexus of agency and health capital

Schneider‐Kamp

2022

SSM - Qualitative Research in Health

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“…A character-level short text classification model based on BERT, a Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, and ERNIE was proposed for eligibility criteria text classification, and they use the focal loss as a loss function to solve the problem of data imbalance among different categories [ 35 ]. SMOTE has also been commonly used in some studies to solve the problem of data imbalance by oversampling some minority classes [ 36 ]. The above methods brought us insights to solve the problem of data imbalance in the subsequent multicategory study.…”

Section: Discussionmentioning

confidence: 99%

A Comparative Study of Natural Language Processing Algorithms Based on Cities Changing Diabetes Vulnerability Data

et al. 2022

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(1) Background: Poor adherence to management behaviors in Chinese Type 2 diabetes mellitus (T2DM) patients leads to an uncontrolled prognosis of diabetes, which results in significant economic costs for China. It is imperative to quickly locate vulnerability factors in the management behavior of patients with T2DM. (2) Methods: In this study, a thematic analysis of the collected interview materials was conducted to construct the themes of T2DM management vulnerability. We explored the applicability of the pre-trained models based on the evaluation metrics in text classification. (3) Results: We constructed 12 themes of vulnerability related to the health and well-being of people with T2DM in Tianjin. We considered that Bidirectional Encoder Representation from Transformers (BERT) performed better in this Natural Language Processing (NLP) task with a shorter completion time. With the splitting ratio of 6:3:1 and batch size of 64 for BERT, the test accuracy was 97.71%, the completion time was 10 min 24 s, and the macro-F1 score was 0.9752. (4) Conclusions: Our results proved the applicability of NLP techniques in this specific Chinese-language medical environment. We filled the knowledge gap in the application of NLP technologies in diabetes management. Our study provided strong support for using NLP techniques to rapidly locate vulnerability factors in T2DM management.

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“…Based on the automatic-identification model of reasons for COVID-19 vaccine hesitation and the twelve types of COVID-19 vaccine hesitation reasons model developed in previous studies, we classified the valid posts [79], in which 12,703 posts were, respectively, identified as one of the twelve types of vaccine-hesitation reasons. After classification, the distribution of the number of valid posts among the twelve types of COVID-19 vaccine hesitation reason is shown in Figure 1.…”

Section: Data Collectionmentioning

confidence: 99%

Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms

Liu

Zheng

2023

Healthcare

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(1) Background: The COVID-19 epidemic is still global and no specific drug has been developed for COVID-19. Vaccination can both prevent infection and limit the spread of the epidemic. Eliminating hesitation to the COVID-19 vaccine and achieving early herd immunity is a common goal for all countries. However, efforts in this area have not been significant and there is still a long way to go to eliminate vaccine hesitancy. (2) Objective: This study aimed to uncover differences in the characteristics and sentiments of COVID-19 vaccine hesitators on Chinese social-media platforms and to achieve a classification of vaccine-hesitant groups. (3) Methods: COVID-19-vaccine-hesitation posts and user characteristics were collected on the Sina Microblog platform for posting times spanning one year, and posts were identified for hesitation types. Logistic regression was used to conduct user-group analysis. The differences in user characteristics between the various types of COVID-19 vaccine posts were analysed according to four user characteristics: gender, address type, degree of personal-information disclosure, and whether they followed health topics. Sentiment analysis was conducted using sentiment analysis tools to calculate the sentiment scores and sentiment polarity of various COVID-19 vaccine posts, and the K–W test was used to uncover the sentiment differences between various types of COVID-19-vaccine-hesitation posts. (4) Results: There are differences in the types of COVID-19-vaccine-hesitation posts posted by users with different characteristics, and different types of COVID-19-vaccine-hesitation posts differ in terms of sentiment. Differences in user attributes and user behaviors are found across the different COVID-19-vaccine-hesitation types. Ultimately, two COVID-19-vaccine-hesitant user groups were identified: Body-related and Non-bodily-related. Users who posted body-related vaccine-hesitation posts are more often female, disclose more personal information and follow health topics on social-media platforms. Users who posted non-bodily-related posts are more often male, disclose less personal information, and do not follow health topics. The average sentiment score for all COVID-19-vaccine-hesitant-type posts is less than 0.45, with negative-sentiment posts outweighing positive- and neutral-sentiment posts in each type, among which the “Individual rights” type is the most negative. (5) Conclusions: This paper complements the application of user groups in the field of vaccine hesitation, and the results of the analysis of group characteristics and post sentiment can help to provide an in-depth and comprehensive analysis of the concerns and needs of COVID-19 vaccine hesitators. This will help public-health agencies to implement more targeted strategies to eliminate vaccine hesitancy and improve their work related to the COVID-19 vaccine, with far-reaching implications for COVID-19-vaccine promotion and vaccination.

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Exploring and Monitoring the Reasons for Hesitation with COVID-19 Vaccine Based on Social-Platform Text and Classification Algorithms

Cited by 11 publications

References 52 publications

COVID-19 Vaccine Hesitancy in Denmark and Russia: A qualitative typology at the nexus of agency and health capital

COVID-19 Vaccine Hesitancy in Denmark and Russia: A qualitative typology at the nexus of agency and health capital

A Comparative Study of Natural Language Processing Algorithms Based on Cities Changing Diabetes Vulnerability Data

Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms

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