A BIBLIOMETRIC ANALYSIS ON THE DISSEMINATION OF COVID -19 VACCINE MISINFORMATION ON SOCIAL MEDIA

In recent years, COVID-19 vaccine-related issues and viewpoints have aroused significant anxiety and concern. Several research studies are extracting, tracking, and evaluating prevalent public opinions on social media and making efforts to curb the misinformation spread. But, there is still a large audience that perceives vaccination as a threat, which in turn reduces our ability to fight effectively against the pandemic. This bibliometric study aims to explore the distribution of capabilities of researchers, institutions, and countries, research themes, and frontiers of Covid-19 vaccine-related misinformation trending on social media since the rollout of these vaccines. The Scopus online database was used for analysis. Excel 2016 and VOSViewer (version 1.6.17) software were used to report the visualizations of infodemic literature on COVID Vaccine on social media. Annual publications, top contributing authors, top-cited journals and author affiliation, leading subject areas, the top country in publication, and keyword network were among the key findings. Future researchers can use these findings to create a baseline before studying Covid-19 vaccine misinformation on social media. Furthermore, it may help in compiling crucial knowledge, trends, and lessons from existing researches to provide useful insights to handle similar phenomena in the future.

A Study on the Topic-Sentiment Evolution and Diffusion in Time Series of Public Opinion Derived from Emergencies

With the development of the Internet, social media has become an important platform for people to deal with emergencies and share information. When a public health emergency occurs, the public can understand the topics of the event and perceive the sentiments of others through social media, thus building a cooperative communication network. In this study, we took the public health emergency as the main research object and the natural disaster, accident, and social security event as the secondary research object and further revealed the law of the formation and evolution of public opinion through the analysis on temporal networks of topics and sentiments in social media platforms. Firstly, we identified the derived topics by constructing the topic model and used the sentiment classification model to divide the text sentiments of the derived topics into two types: positive sentiment and negative sentiment. Then, the ARIMA time series model was used to fit and predict the evolution and diffusion rules of topics and sentiments derived from public opinions on temporal networks. It was found that the evolution law of derived public opinions had similarities and differences in various types of emergencies and was closely related to government measures and media reports. The related research provides a foundation for the management of network public opinion and the realization of better emergency effects.

Analysis and Mining of Internet Public Opinion Based on LDA Subject Classification

This paper uses Python, R language, Gephi and other software to crawl and classify the comment content of Weibo hot search events. Using word cloud, co-occurrence social network graphs, LDA topic classification visualization methods, this paper regularizes and integrates public opinions of hot events. Through this research, we can get the influence of public opinion mediators, public opinion objects, and government forces on the network public opinion and put forward corresponding improvement suggestions. We hope to contribute to the government’s governance and prevention of online public opinion during the spread of COVID-19 and other public hot events.

Finding Clusters of Similar-minded People on Twitter Regarding the Covid-19 Pandemic

Two clustering methods to determine users with similar opinions on the Covid-19 pandemic and the related public debate in Germany will be presented in this paper. We believe, they can helpgaining an overview over similar-minded groups and could support the prevention of fake-news distribution. The first method uses a new approach to create a network based on retweetrelationships between users and the most retweeted users, the so-called influencers. The second method extracts hashtags from users posts to create a “user feature vector” which is then clustered, using a consensus matrix based on previous work, to identify groups using the same language. With both approaches it was possible to identify clusters that seem to fit groups of different public opinions in Germany. However, we also found that clusters from one approach cannot be associated with clusters from the other due to filtering steps in the two methods.