ArticleIEEE access : practical innovations, open solutions2020
Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning.
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 21 papers.
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Who cites it
21 citing papers in PubMed.
- The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review.Health science reports · 2024Review
- Evaluation of online videos to engage viewers and support decision-making for COVID-19 vaccination: how narratives and race/ethnicity enhance viewer experiences.Frontiers in public health · 2023Article
- Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign.PloS one · 2023Article
- Towards a soft three-level voting model (Soft T-LVM) for fake news detection.Journal of intelligent information systems · 2022Article
- The use of the Dark Web as a COVID-19 information source: A three-country study.Technology in society · 2022Article
- Covid-19 fake news sentiment analysis.Computers & electrical engineering : an international journal · 2022Article
- COVID-19 vaccine hesitancy: a social media analysis using deep learning.Annals of operations research · 2022Article
- Sentinel node approach to monitoring online COVID-19 misinformation.Scientific reports · 2022Article
- Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature.EClinicalMedicine · 2022Article
- An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach.JMIR medical informatics · 2022Article
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
- Sentiment analysis of COVID-19 social media data through machine learning.Multimedia tools and applications · 2022Article
- Article
- Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments.Computational intelligence and neuroscience · 2022Article
- Perceptions and Feelings of Brazilian Health Care Professionals Regarding the Effects of COVID-19: Cross-sectional Web-Based Survey.JMIR formative research · 2021Article
- Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms.Scientific reports · 2021Article
- Hidden order across online extremist movements can be disrupted by nudging collective chemistry.Scientific reports · 2021Article
- CoVerifi: A COVID-19 news verification system.Online social networks and media · 2021Article
- Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.JMIR medical informatics · 2021Review
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations ("anti-vax"). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination ("pro-vax") community. However, the anti-vax community exhibits a broader range of "flavors" of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation.
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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.