Exploring the spread and impact of health-related misinformation on Chinese and English social media during the COVID-19 pandemic: a comparative bibliometric study (Preprint)

This is the authors' abstract. We don't add key points for this paper.

BACKGROUND Online health-related misinformation poses a serious threat to public health. As the coronavirus disease 2019 (COVID-19) pandemic aggravated the spread of misinformation, relevant research has surged. OBJECTIVE To systematically summarize Chinese and English articles regarding health-related misinformation on social media during the COVID-19 pandemic and quantitatively describe research progress. METHODS Using bibliometrics, we systematically analyzed and compared the characteristics of scientific articles in English and Chinese regarding article numbers, journals, authors, countries, institutions, funding, and research topics and compared changes in hot topics. RESULTS This study included 1,241 articles. Article numbers and citations surged during the COVID-19 pandemic (1.80 times and 2.94 times, respectively, compared to pre-pandemic data), but high-impact articles were lacking. The field lacked a core group of authors and collaborative networks. China was the country with the largest number of papers (n=265) and funds (n=284), but articles in English exceeded by far those in Chinese (1,078 vs. 163, respectively). Regarding article topics before and after the COVID-19 pandemic, the transformation from qualitative small-data analyses to quantitative empirical big-data research has been realized. CONCLUSIONS With the maturity of natural language processing technology, in-depth mining of massive user-generated content has become a hot spot. The outbreak of the COVID-19 pandemic has prompted the research focus to shift from misinformation-related health problems to social problems involving the sources, content, channels, audiences, and effects of communication networks. Using artificial intelligence technology like machine learning to deeply mine large amounts of user-generated content on social media will be a future research hot spot.