Background: Online health-related misinformation poses a serious threat to public health. As the coronavirus disease 2019 (COVID19) pandemic aggravated the spread of misinformation regarding COVID-19, relevant research has surged. Objective: To systematically summarize Chinese and English articles regarding health-related misinformation about COVID-19 on social media and quantitatively describe research progress. Methods: Using bibliometrics, we systematically analyzed and compared the characteristics of scientific articles in English and Chinese, examining article numbers, journals, authors, countries, institutions, funding, and research topics, and compared changes in popular research topics. Results: This study analyzed 1,294 articles, revealing a significant increase in article numbers and citations during the COVID-19 pandemic (1.94 times and 2.95 times, respectively, compared to pre-pandemic data). However, high-impact articles were scarce and the field lacked a core group of authors and collaborative networks. China had the largest number of papers (n=266) and funds (n=292), but articles in English exceeded by far those in Chinese (1,131 vs 163, respectively). Regarding article topics, the transformation from qualitative small-data analyses to quantitative empirical big-data research has been realized. Conclusion: 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.