ArticleJournal of medical Internet research2020
COVID-19 and the "Film Your Hospital" Conspiracy Theory: Social Network Analysis of Twitter Data.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses 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
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Antecedents and consequences of COVID-19 conspiracy beliefs: A systematic review.Social science & medicine (1982) · 2022Pooled it
- Assessment of the Effectiveness of Identity-Based Public Health Announcements in Increasing the Likelihood of Complying With COVID-19 Guidelines: Randomized Controlled Cross-sectional Web-Based Study.JMIR public health and surveillance · 2021Trial
- Impact of a Serious Game (Escape COVID-19) on the Intention to Change COVID-19 Control Practices Among Employees of Long-term Care Facilities: Web-Based Randomized Controlled Trial.Journal of medical Internet research · 2021Trial
- Regional and temporal patterns of partisan polarization during the COVID-19 pandemic in the United States and Canada.PloS one · 2026Article
- Mapping Vaccine Sentiment by Analyzing Spanish-Language Social Media Posts and Survey-Based Public Opinion: Dual Methods Study.JMIR infodemiology · 2025Observational
- Organ Donation Conversations on X and Development of the OrgReach Social Media Marketing Strategy: Social Network Analysis.Journal of medical Internet research · 2025Article
- Unraveling the Use of Disinformation Hashtags by Social Bots During the COVID-19 Pandemic: Social Networks Analysis.JMIR infodemiology · 2025Article
- Serious Game Development for Public Health: Participatory Design Approach to COVID-19 Quarantine Policy Education.JMIR serious games · 2024Article
- Racial Biases Associated With Pulse Oximetry: Longitudinal Social Network Analysis of Social Media Advocacy Impact.Journal of medical Internet research · 2024Article
- The Influence of Joe Wicks on Physical Activity During the COVID-19 Pandemic: Thematic, Location, and Social Network Analysis of X Data.Journal of medical Internet research · 2024Article
- Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.PloS one · 2024Article
- A cross-lingual analysis on the spread of misinformation using the case of Ivermectin as a treatment for Covid-19.Scientific reports · 2023Article
- Information Circulation Among Spanish-Speaking and Caribbean Communities Related to COVID-19: Social Media-Based Multidimensional Analysis.Journal of medical Internet research · 2023Article
- Health Literacy and Critical Lecture as Key Elements to Detect and Reply to Nutrition Misinformation on Social Media: Analysis between Spanish Healthcare Professionals.International journal of environmental research and public health · 2022Article
- Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic.International journal of environmental research and public health · 2022Article
- When the influencer says jump! How influencer signaling affects engagement with COVID-19 misinformation.Social science & medicine (1982) · 2022Article
- Ecosystem Services: A Social and Semantic Network Analysis of Public Opinion on Twitter.International journal of environmental research and public health · 2022Article
- The #VaccinesWork Hashtag on Twitter in the Context of the COVID-19 Pandemic: Network Analysis.JMIR public health and surveillance · 2022Article
- [Conspiracy theories and COVID-19: How do conspiracy beliefs arise?]L'Encephale · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDuring the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are "empty." Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it.
objectiveThis study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future.
methodsTwitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots.
resultsThe most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network.
conclusionsHashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.
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.