ArticleApplied soft computing2021
Cross-SEAN: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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
23 citing papers in PubMed.
- Identifying Disinformation on the Extended Impacts of COVID-19: Methodological Investigation Using a Fuzzy Ranking Ensemble of Natural Language Processing Models.Journal of medical Internet research · 2025Article
- Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Linguistic indicators for predicting the veracity of online health rumors.Frontiers in 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
- A review on fake news detection 3T's: typology, time of detection, taxonomies.International journal of information security · 2023Article
- An automated multi-web platform voting framework to predict misleading information proliferated during COVID-19 outbreak using ensemble method.Data & knowledge engineering · 2023Article
- CovTiNet: Covid text identification network using attention-based positional embedding feature fusion.Neural computing & applications · 2023Article
- A comparative analysis of the COVID-19 Infodemic in English and Chinese: insights from social media textual data.Frontiers in public health · 2023Article
- An optimal control policy in fighting COVID-19 and infectious diseases.Applied soft computing · 2022Article
- Review on the COVID-19 pandemic prevention and control system based on AI.Engineering applications of artificial intelligence · 2022Review
- An intelligent cybersecurity system for detecting fake news in social media websites.Soft computing · 2022Article
- Detection and moderation of detrimental content on social media platforms: current status and future directions.Social network analysis and mining · 2022Review
- Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news.The European physical journal. Special topics · 2022Article
- A systematic literature review and existing challenges toward fake news detection models.Social network analysis and mining · 2022Article
- Applications of machine learning for COVID-19 misinformation: a systematic review.Social network analysis and mining · 2022Review
- Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing.JMIR public health and surveillance · 2021Article
- Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining.International journal of environmental research and public health · 2021Review
- A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets.Online social networks and media · 2021Article
- Release and Demand of Public Health Information in Social Media During the Outbreak of COVID-19 in China.Frontiers in public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vital for public health and safety, COVID-19 related fake news has been spreading even faster than the facts. During times such as the COVID-19 pandemic, fake news can not only cause intellectual confusion but can also place people's lives at risk. This calls for an immediate need to contain the spread of such misinformation on social media. We introduce
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.