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
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.
Authors
- Jianbo Lei (ORCID: https://orcid.org/0000-0002-1744-0235)
- Jun Liang (ORCID: https://orcid.org/0000-0002-0551-6706)
- Wenguang Fu (ORCID: https://orcid.org/0000-0003-3672-9728)
- Yunfan He (ORCID: https://orcid.org/0000-0001-5539-5044)
- Shunjing Ding
- Fangyu Yang (ORCID: https://orcid.org/0009-0001-1961-2387)
- Yongcheng Liu (ORCID: https://orcid.org/0009-0006-7663-6322)
Institutions
- Capital Medical University (CN)
- Peking University (CN)
- Fudan University (CN)
- Southwest Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- Beijing Tian Tan Hospital (CN)
- NetEase (China) (CN)
- Peking University People's Hospital (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Journal of Multidisciplinary Healthcare
- Published
- 2024-12-01
- DOI
- https://doi.org/10.2147/jmdh.s501067
- Citations
- 1
- Primary Topic
- Misinformation and Its Impacts
- Type
- article
- Field-Weighted Citation Impact
- 1.13
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Zhejiang Province