(1) Background: The COVID-19 pandemic is globally rampant, and it is the common goal of all countries to eliminate hesitation in taking the COVID-19 vaccine and achieve herd immunity as soon as possible. However, people are generally more hesitant about the COVID-19 vaccine than about other conventional vaccines, and exploring the specific reasons for hesitation with the COVID-19 vaccine is crucial. (2) Methods: this paper selected text data from a social platform to conduct qualitative analysis of the text to structure COVID-19 vaccine hesitancy reasons, and then conducted semiautomatic quantitative content analysis of the text through a supervised machine-learning method to classify them. (3) Results: on the basis of a large number of studies and news reports on vaccine hesitancy, we structured 12 types of the COVID-19 vaccine hesitancy reasons. Then, in the experiment, we conducted comparative analysis of three classifiers: support vector machine (SVM), logistic regression (LR), and naive Bayes classifier (NBC). Results show that the SVM classification model with TF-IDF and SMOTE had the best performance. (4) Conclusions: our study structured 12 types of COVID-19 vaccine hesitancy reasons through qualitative analysis, filling in the gaps of previous studies. At the same time, this work provides public health institutions with a monitoring tool to support efforts to mitigate and eliminate COVID-19 vaccine hesitancy.
(1) Background: Abundant evidence has shown that the COVID-19 vaccine booster is highly effective against the Omicron variant. It is of great practical significance to explore the factors influencing the intention to receive COVID-19 booster shots. (2) Methods: We introduced expectation confirmation theory as the basis to construct a model of the factors of the vaccination intention for COVID-19 vaccine boosters. We obtained two batches of questionnaires through Chinese social platforms, with a valid sample size of 572. To test the model, we used SmartPLS3.0 software for empirical analysis. (3) Results: In terms of the characteristics of the vaccine itself, perceived vaccine efficacy and perceived vaccine safety had significant positive effects on expectation confirmation. Regarding vaccination services, perceived vaccination convenience also had a significant positive effect on expectation confirmation. Expectation confirmation positively affected the vaccination intention for the COVID-19 vaccine boosters. Furthermore, the results showed two moderating effects: first, health consciousness negatively moderated the positive effect of perceived vaccine safety on expectation confirmation; second, the time interval since the last dose negatively moderated the positive effect of perceived vaccine efficacy on expectation confirmation. (4) Conclusions: Our research demonstrated that there is an expectation confirmation process for previous COVID-19 vaccines before people consider whether to obtain a booster shot. Perceived vaccine efficacy and perceived vaccine safety remained important factors in receiving COVID-19 booster shots, and our conclusions were consistent with previous literature. In this study, multiple dimensions such as distance and cost were used to measure perceived vaccination convenience. This new variable improve the explanatory power of the convenience of the vaccination service and enrich the variables of the factor model of vaccination intention. In addition, the moderating effects of health consciousness and time interval were found. The findings can provide a theoretical reference for public health institutions to help them understand the formation process of people’s intention to receive the COVID-19 vaccine booster.
(1) Background: During the COVID-19 pandemic, users share and obtain COVID-19 information through video platforms, but only a few COVID-19 videos become popular among most audiences. Therefore, it is a very interesting and important research question to explore the influencing factors of the popularity of COVID-19 videos during the COVID-19 pandemic; (2) Our research collects video data related to the keyword “COVID-19” on video platform, the data are analyzed by content analysis and empirical analysis. We then constructed a theoretical model based on the information adoption model; (3) A total of 251 videos were divided into three categories. The least common category was the data and analysis category (11.2%), followed by the prevention and control status category (13.5%); the knowledge and general science category was the most common (75.3%). From the perspective of video quality, the information sources of most videos are relatively reliable, and the content of medical information is low. The research results showed that short video lengths, longer descriptions, more reliable video sources and lower medical information content were more popular with audiences. Audiences are more likely to be attracted to videos in the prevention and control status category and knowledge and general science category. Videos uploaded by uploaders who have a higher influence are more popular with audiences; (4) Conclusion: During the COVID-19 pandemic, information quality (video length, description length, video content type, and medical information and content index) and source credibility (information source reliability, influence and certification type) all significantly influence the popularity level of COVID-19 videos. Our research conclusions can provide management suggestions for the platform, make videos released by uploaders more popular with audiences, and help audiences better understand COVID-19 information and make prevention and control efforts.
During the COVID-19 epidemic, social media has become the main channel for people to learn information related to the epidemic, among which information in the form of videos has played a significant role in the prevention and control of COVID-19. However, few studies have analyzed the process of knowledge learning of individuals through watching COVID-19 videos. Therefore, to explore the process of COVID-19 video viewers’ knowledge acquisition, this paper constructs a knowledge learning path model based on the cognitive mediation model and dual coding theory. A sample of 255 valid questionnaires was collected to validate this model. The results of this study show that an individual’s perceived risk of COVID-19 affects their surveillance motivation positively, while surveillance motivation further stimulates the attention and elaboration about the information in COVID-19 videos. Among them, attention positively influences the elaboration about the information. Ultimately, both an individual’s attention and elaboration positively influence the knowledge he or she acquires from the COVID-19 videos. This paper not only verifies the hypothesized relationships in the original cognitive mediation model, but also extends the model to the context of video knowledge learning. Analyzing the knowledge learning process of COVID-19 video viewers, this paper can provide suggestions for government propaganda departments and relevant media to improve public knowledge of COVID-19.
(1) Background: The coronavirus variants have posed serious challenges for the prevention and control of the COVID-19 pandemic. Individuals selectively watch and forward videos that help them reduce the damage caused by the virus. Therefore, the factors influencing video viewing and sharing in the context of the COVID-19 pandemic caused by virus variation must be explored. (2) Method: Based on a combination of uncertainty reduction theory and functional emotion theory, this paper designed hypotheses regarding how content relevance and emotional consistency affect video views and shares. We used the support vector machine (SVM) classification algorithm to measure the content relevance between videos and virus variant topics. We performed sentiment analysis of video text to evaluate the emotional consistency between videos and virus variant topics. Then, we used empirical analysis to build the model. (3) Results: The trained SVM classifier was effective in judging whether the video text was related to virus variant topics (F = 88.95%). The content relevance between COVID-19 videos and virus variant topics was generally low. The results showed that the higher the content relevance, the more views (IRR = 1.005, p = 0.017) and shares (IRR = 1.008, p = 0.009) the video received. Individuals were more willing to view (IRR = 1.625, p < 0.001) and share (IRR = 1.761, p < 0.001) COVID-19 videos with high emotional consistency with virus variant topics. (4) Conclusions: The results of empirical analysis showed that content relevance and emotional consistency between videos and virus variant topics significantly positively impacted video views and shares. The trained SVM classifier can support public health departments in monitoring and assessing the COVID-19 pandemic. Our study provides management advice while helping individuals reduce harm and inform next-step decisions.
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