Mapping Knowledge Landscapes and Emerging Trends for the Spread of Health-Related Misinformation During the COVID-19 on Chinese and English Social Media: A Comparative Bibliometric and Visualization Analysis

Yunfan He,1,2 Jun Liang,3– 5 Wenguang Fu,6 Yongcheng Liu,7 Fangyu Yang,8 Shunjing Ding,9 Jianbo Lei10– 12 1School of International Relations and Public Affairs, Fudan University, Shanghai, People’s Republic of China; 2National Institute of Intelligent Evaluation and Governance, Fudan University, Shanghai, People’s Republic of China; 3Department of AI and IT, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China; 4Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China; 5Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China; 6Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, People’s Republic of China; 7Netease Group, Hangzhou, Zhejiang, People’s Republic of China; 8School of Nursing, Capital Medical University, Beijing, People’s Republic of China; 9Department of Cardiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China; 10Clinical Research Center, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 11School of Medical Informatics and Engineering, Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 12Center for Medical Informatics, Health Science Center, Peking University, Beijing, People’s Republic of ChinaCorrespondence: Jianbo Lei, Center for Medical Informatics, Peking University, Beijing, 100191, People’s Republic of China, Tel +86 (10) 8280-5901, Fax +86 (10) 8280-5900, Email [email protected] Shunjing Ding, Department of Cardiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China, Email [email protected]: Online health-related misinformation poses a serious threat to public health. As the coronavirus disease 2019 (COVID-19) 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.Keywords: misinformation, online health information, social media, COVID-19, Chinese, English