Strategies and effectiveness of the chinese government debunking covid-19 rumors on sina weibo: Evaluating from emotions

The Chinese government refuted rumors on social media for infodemic management when COVID-19 outbroke. This study selected 80 government accounts on Sina Weibo and collected 501 valid anti-rumor posts with comments from 18 January to 29 February 2020. This paper evaluated the effectiveness of rumor debunking from the public emotions reflected in the comments. This study also examined the influence of different anti-rumor strategies, such as fact-checking, rumor response modes, and presentation forms, on the effectiveness of rumor debunking. The findings revealed that fact-checking, combined response mode and text presentation could improve the effectiveness of rumor debunking to some extent. Further analysis of the public emotions indicated a correlation between the trust in government and the effectiveness of rumor debunking. These findings suggested building a multiparticipant response mechanism with medical institutions and media to mitigate the COVID-19 infodemic through targeted strategies, thus further increasing the government's credibility via information governance. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

COVID-19 Vaccine Hesitancy in China: An Analysis of Reasons through Mixed Methods.

This study aims to investigate the causes of COVID-19 vaccine hesitancy among the Chinese population. The LDA model and content analysis were used to analyze the content of COVID-19 vaccine hesitancy expressed by the Chinese on Weibo from 2020 to 2022, the leading causes of vaccine hesitancy, and the changes in the reasons for vaccine hesitancy over time. The study found that when the Chinese expressed vaccine hesitancy, it usually involved themes such as information access (18.59%), vaccination services (13.91%), and physical illness (13.24%), and topics such as vaccination process (6.83%), allergic diseases (6.59%), and international news (6.43%). Constraints (35.48%), confidence (17.94%), and calculation (15.99%) are the leading causes of vaccine hesitancy on Weibo. These findings provide a comprehensive picture of how the Chinese express vaccine hesitancy in social media and the reasons and changes for vaccine hesitancy, which can help inspire public health experts, health organizations, or governments in various countries to improve the phenomenon of vaccine hesitancy.

Media Exposure and Media Credibility Influencing Public Intentions for Influenza Vaccination.

Due to the low rate of influenza vaccination in China, this study explores the factors influencing the Chinese public's influenza vaccination intentions. Based on the technology acceptance model (TAM), this study builds a theoretical model to examine the factors influencing Chinese public intentions toward influenza vaccination. We define media exposure and media credibility as external variables and the perceived characteristics of influenza vaccines as intermediate variables in the proposed model. A total of 597 valid questionnaires were collected online in this study. Combined with structural equation modeling (SEM), SPSS 22.0 and AMOS 17.0 were used to conduct empirical research, supporting the proposed research hypotheses. The results show that media exposure and media credibility have no direct effects on the audience's intention to take the influenza vaccine. However, media exposure positively influences media credibility, influencing vaccination intentions through perceived usefulness (PU) and perceived ease of use (PEOU). Furthermore, PU and PEOU significantly positively influence behavioral intentions, and PEOU significantly affects PU. This paper has proven that media with better credibility gained more trust from the audience, indicating a new perspective for the promotion of influenza vaccination. This study suggests releasing influenza-related information via media with great credibility, further improving public acceptance of becoming vaccinated.

Does the COVID-19 Vaccine Still Work That "Most of the Confirmed Cases Had Been Vaccinated"? A Content Analysis of Vaccine Effectiveness Discussion on Sina Weibo during the Outbreak of COVID-19 in Nanjing.

In July 2021, breakthrough cases were reported in the outbreak of COVID-19 in Nanjing, sparking concern and discussion about the vaccine's effectiveness and becoming a trending topic on Sina Weibo. In order to explore public attitudes towards the COVID-19 vaccine and their emotional orientations, we collected 1542 posts under the trending topic through data mining. We set up four categories of attitudes towards COVID-19 vaccines, and used a big data analysis tool to code and manually checked the coding results to complete the content analysis. The results showed that 45.14% of the Weibo posts (n = 1542) supported the COVID-19 vaccine, 12.97% were neutral, and 7.26% were doubtful, which indicated that the public did not question the vaccine's effectiveness due to the breakthrough cases in Nanjing. There were 66.47% posts that reflected significant negative emotions. Among these, 50.44% of posts with negative emotions were directed towards the media, 25.07% towards the posting users, and 11.51% towards the public, which indicated that the negative emotions were not directed towards the COVID-19 vaccine. External sources outside the vaccine might cause vaccine hesitancy. Public opinions expressed in online media reflect the public's cognition and attitude towards vaccines and their core needs in terms of information. Therefore, online public opinion monitoring could be an essential way to understand the opinions and attitudes towards public health issues.

Changes of the Public Attitudes of China to Domestic COVID-19 Vaccination After the Vaccines Were Approved: A Semantic Network and Sentiment Analysis Based on Sina Weibo Texts.

Introduction: On December 31, 2020, the Chinese government announced that the domestic coronavirus disease-2019 (COVID-19) vaccines have obtained approval for conditional marketing and are free for vaccination. This release drove the attention of the public and intense debates on social media, which reflected public attitudes to the domestic vaccine. This study examines whether the public concerns and public attitudes to domestic COVID-19 vaccines changed after the official announcement. Methods: This article used big data analytics in the research, including semantic network and sentiment analysis. The purpose of the semantic network is to obtain the public concerns about domestic vaccines. Sentiment analysis reflects the sentiments of the public to the domestic vaccines and the emotional changes by comparing the specific sentiments shown on the posts before and after the official announcement. Results: There exists a correlation between the public concerns about domestic vaccines before and after the official announcement. According to the semantic network analysis, the public concerns about vaccines have changed after the official announcement. The public focused on the performance issues of the vaccines before the official approval, but they cared more about the practical issues of vaccination after that. The sentiment analysis showed that both positive and negative emotions increased among the public after the official announcement. "Good" was the most increased positive emotion and indicated great public appreciation for the production capacity and free vaccination. "Fear" was the significantly increased negative emotion and reflected the public concern about the safety of the vaccines. Conclusion: The official announcement of the approval for marketing improved the Chinese public acceptance of the domestic COVID-19 vaccines. In addition, safety and effectiveness are vital factors influencing public vaccine hesitancy.

Weibo Users' Emotion and Sentiment Orientation in Traditional Chinese Medicine (TCM) During the COVID-19 Pandemic.

OBJECTIVE: This study aimed to explore Chinese people's attitudes to the official application of TCM in coronavirus disease 2019 (COVID-19) treatment. METHODS: We collected data referring to TCM on Weibo from 0:00 on January 24, 2020, to 23:59:59 on March 31, 2020 (Beijing time). In addition, this study used DLUT-Emotion ontology to analyze the sentiment orientation and emotions of selected data and then conducted a text analysis. RESULTS: According to DLUT-Emotion ontology, we examined 3 sentiment orientations of 215,565 valid Weibo posts. Among them, 25,025 posts were judged as positive emotions, accounting for approximately 12%; 22,362 were regarded as negative emotions, accounting for approximately 10%; and 168,178 were judged as neutral emotions, accounting for approximately 78%. Results indicate that the words judged as "Good" have the highest frequency, and words marked as "Happy" have increased over time. The word frequency of "Fear" and "Sadness" showed a significant downward trend. CONCLUSION: Weibo users have a relatively positive attitude to the TCM in the COVID-19 treatment in general. Results of text analysis show that data with negative emotions is essentially an expression of public opinions to supporting TCM or not. Texts of "Fear" and "Sadness" do not reflect users' negative attitudes to TCM.