ArticleBMC research notes2021
Designing a conceptual framework for misinformation on social media: a qualitative study on COVID-19.
Article in BMC research notes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Development of a Conceptual Framework of Health Misinformation During the COVID-19 Pandemic: Systematic Review of Reviews.JMIR public health and surveillance · 2025Pooled it
- From emergence to amplification: an analysis of lifecycle models to address health mis-disinformation in the digital environment.Frontiers in medicine · 2026Review
- #Coronavirus on TikTok: user engagement with misinformation as a potential threat to public health behavior.JAMIA open · 2023Article
- Misinformation about COVID-19 among middle-aged and older migrants residing in Brazil and Portugal.Revista da Escola de Enfermagem da U S P · 2023Article
- Agreement with COVID-19 disinformation among Portuguese-speaking older adults: an international study.Revista brasileira de enfermagem · 2023Article
- COVID-19-Related Rumor Content, Transmission, and Clarification Strategies in China: Descriptive Study.Journal of medical Internet research · 2021Article
- The Impact of Information Sources on COVID-19-Related Knowledge, Attitudes, and Practices (KAP) among University Students: A Nationwide Cross-Sectional Study.International journal of environmental research and public health · 2021Article
- Health Information Disorders Models: A Scoping Review.Iranian journal of nursing and midwifery researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study was aimed to present a conceptual framework about the misinformation surrounding COVID-19 outbreak in Iran. For this purpose, discourse analysis of two of the most common social virtual networks were conducted via a four step approach as follows: defining the research question and selecting the content of analysis, gathering information and theory on the context, content analysis for establishing the themes and patterns and, presenting the results and drawing conclusions.
resultsCultural factors, demand pressure for information during the crisis, the easiness of information dissemination via social networks, marketing incentives and the poor legal supervision of online content are the main reasons for misinformation dissemination. Disease statistics; treatments and prevention are the main subjective categories of releasing misinformation. The consequences of misinformation dissemination include psychosocial, economic, health status, health system and ethical ones. The most recommended strategies for dealing with the issue could be divided into demand and supply-side strategies.
Indexed as
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.