ArticleIEEE access : practical innovations, open solutions2020
Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Article
- Bibliometric Insights Into the Infodemic: Global Research Trends and Policy Responses: Quantitative Research.JMIR medical informatics · 2025Article
- Leveraging federated learning and edge computing for pandemic-resilient healthcare.Scientific reports · 2025Article
- Identifying Misinformation About Unproven Cancer Treatments on Social Media Using User-Friendly Linguistic Characteristics: Content Analysis.JMIR infodemiology · 2025Article
- Article
- Association Between X/Twitter and Prescribing Behavior During the COVID-19 Pandemic: Retrospective Ecological Study.JMIR infodemiology · 2024Article
- Real-time fake news detection in online social networks: FANDC Cloud-based system.Scientific reports · 2024Article
- Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.PloS one · 2024Article
- Review
- Infodemic: Challenges and solutions in topic discovery and data process.Archives of public health = Archives belges de sante publique · 2023Article
- Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic.Decision support systems · 2023Article
- Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users' Comments.Sensors (Basel, Switzerland) · 2023Article
- Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification.JMIR infodemiology · 2023Article
- Credibility of vaccine-related content on Twitter during COVID-19 pandemic.PLOS global public health · 2023Article
- A survey of uncover misleading and cyberbullying on social media for public health.Cluster computing · 2023Review
- A Novel Hybrid Multi-Modal Deep Learning for Detecting Hashtag Incongruity on Social Media.Sensors (Basel, Switzerland) · 2022Article
- What predicts people's belief in COVID-19 misinformation? A retrospective study using a nationwide online survey among adults residing in the United States.BMC public health · 2022Article
- Human Mobility Restrictions and COVID-19 Infection Rates: Analysis of Mobility Data and Coronavirus Spread in Poland and Portugal.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Online social networks (ONSs) such as Twitter have grown to be very useful tools for the dissemination of information. However, they have also become a fertile ground for the spread of false information, particularly regarding the ongoing coronavirus disease 2019 (COVID-19) pandemic. Best described as an infodemic, there is a great need, now more than ever, for scientific fact-checking and misinformation detection regarding the dangers posed by these tools with regards to COVID-19. In this article, we analyze the credibility of information shared on Twitter pertaining the COVID-19 pandemic. For our analysis, we propose an ensemble-learning-based framework for verifying the credibility of a vast number of tweets. In particular, we carry out analyses of a large dataset of tweets conveying information regarding COVID-19. In our approach, we classify the information into two categories: credible or non-credible. Our classifications of tweet credibility are based on various features, including tweet- and user-level features. We conduct multiple experiments on the collected and labeled dataset. The results obtained with the proposed framework reveal high accuracy in detecting credible and non-credible tweets containing COVID-19 information.
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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.