ArticleJournal of multidisciplinary healthcare2024
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.
Article in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence and Digital Technologies Against Health Misinformation: A Scoping Review of Public Health Responses.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.