ArticleOnline social networks and media2021
CoVerifi: A COVID-19 news verification system.
Article in Online social networks and media, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed.
- Examining hurricane-related social media topics longitudinally and at scale: A transformer-based approach.PloS one · 2025Article
- Combating the COVID-19 infodemic using Prompt-Based curriculum learning.Expert systems with applications · 2023Article
- The Influence of News Consumption Habits and Dispositional Traits on Trust in Medical Scientists.International journal of environmental research and public health · 2023Article
- Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review.Neural computing & applications · 2023Article
- CovTiNet: Covid text identification network using attention-based positional embedding feature fusion.Neural computing & applications · 2023Article
- Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.International journal of data science and analytics · 2023Article
- Article
- Opinions on Homeopathy for COVID-19 on Twitter.Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference · 2022Article
- Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations.PeerJ. Computer science · 2022Article
- Sociodemographic Predictors Associated with the Willingness to Get Vaccinated against COVID-19 in Peru: A Cross-Sectional Survey.Vaccines · 2021Article
- Switching infliximab in psoriatic patients during COVID-19 pandemics: A real-life retrospective study comparing intra-versus interclass switching strategies.Dermatologic therapy · 2021Observational
- COVID-19 Misinformation Online and Health Literacy: A Brief Overview.International journal of environmental research and public health · 2021Review
- Telemedicine in Peru as a Result of the COVID-19 Pandemic: Perspective from a Country with Limited Internet Access.The American journal of tropical medicine and hygiene · 2021Article
- A Framework of AI-Based Approaches to Improving eHealth Literacy and Combating Infodemic.Frontiers in public health · 2021Review
- Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.Computational and mathematical methods in medicine · 2021Article
- Data Exploration and Classification of News Article Reliability: Deep Learning Study.JMIR infodemiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is an abundance of misinformation, disinformation, and "fake news" related to COVID-19, leading the director-general of the World Health Organization to term this an 'infodemic'. Given the high volume of COVID-19 content on the Internet, many find it difficult to evaluate veracity. Vulnerable and marginalized groups are being misinformed and subject to high levels of stress. Riots and panic buying have also taken place due to "fake news". However, individual research-led websites can make a major difference in terms of providing accurate information. For example, the Johns Hopkins Coronavirus Resource Center website has over 81 million entries linked to it on Google. With the outbreak of COVID-19 and the knowledge that deceptive news has the potential to measurably affect the beliefs of the public, new strategies are needed to prevent the spread of misinformation. This study seeks to make a timely intervention to the information landscape through a COVID-19 "fake news", misinformation, and disinformation website. In this article, we introduce CoVerifi, a web application which combines both the power of machine learning and the power of human feedback to assess the credibility of news. By allowing users the ability to "vote" on news content, the CoVerifi platform will allow us to release labelled data as open source, which will enable further research on preventing the spread of COVID-19-related misinformation. We discuss the development of CoVerifi and the potential utility of deploying the system at scale for combating the COVID-19 "infodemic".
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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.