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. 2024 Dec 19:17:6043-6057.
doi: 10.2147/JMDH.S501067. eCollection 2024.

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   4   5 , Wenguang Fu  6 , Yongcheng Liu  7 , Fangyu Yang  8 , Shunjing Ding  9 , Jianbo Lei  10   11   12
Affiliations

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 et al. J Multidiscip Healthc. .

Abstract

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 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: COVID-19; Chinese; English; misinformation; online health information; social media.

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Conflict of interest statement

The authors declare no conflicts of interest. This paper has been uploaded to JMIR as a preprint: https://preprints.jmir.org/preprint/49268.

Figures

Figure 1
Figure 1
Flowchart of the article screening process.
Figure 2
Figure 2
Number of scientific publications per quarter.
Figure 3
Figure 3
Citation of the scientific production per quarter.
Figure 4
Figure 4
Number of articles per quarter published by the top 5 core journals.
Figure 5
Figure 5
Comparison of the citation frequency of the top 5 core journals per quarter.
Figure 6
Figure 6
Publishing trends of the top 10 authors. Marker size and color shade indicate the annual numbers of publications and citations, respectively.
Figure 7
Figure 7
Distribution of the number of individual and cooperative publication of the top 10 high-yield countries.
Figure 8
Figure 8
Cooperation networks of research institutions.
Figure 9
Figure 9
High-frequency keywords co-occurrence network.
Figure 10
Figure 10
Thematic map per domain.
Figure 11
Figure 11
Sankey diagram showing changes in thematic trends from 2020 to 2022.
See this image and copyright information in PMC

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