COVID-19 related network public opinion on the Chinese microblogging platform Weibo: A bibliometric assessment using data from the Web of Science (Preprint)

Abstract

UNSTRUCTURED COVID-19 has posed a huge challenge to almost all countries in the world, and its impact on the mentality of society is still ongoing. Exploring the characteristics of network public opinion and conducting effective risk communication are essential aspects of responding to such pandemics. By using bibliometric methods, this study explored the current state of COVID-19-related network public opinion research on Chinese microblogging platform Weibo in the Web of Science Core Collection database. Functions such as collaborative network, word cloud, heat map in R were used to systematically analyze the collaboration situations, highly cited articles, and keywords of the included studies. A total of 281 articles, including 275 research articles and 6 reviews, published in 148 academic journals were collected for the bibliometric investigation. Keyword analysis showed that the relevant topics focused on ‘emotion’, ‘communication’, and ‘infodemiology’. In the late stage of the COVID-19 pandemic, research focus shifted from conceptual descriptions to more diversified and subdivided fields. ‘Content analysis’ and ‘machine learning’ were the most popular methods. For future research, international cooperation should be emphasized, more comprehensive and flexible research methods should also be applied, such as combination of ‘content analysis’ and ‘machine learning’, and ‘MCDM’. In addition, more emphasis should be placed on dialogue and communication skills towards specific populations. In conclusion, this is the first bibliometric assessment to systematically dissect the research status of COVID-19-related network public opinion on Chinese microblogging platform Weibo, which may provide valuable references for subsequent research directions.