Leveraging contextual features to enhanced machine learning models in detecting COVID-19 fake news

Qasem, Amal Esmail; Sajid, Mohammad

doi:10.1007/s41870-023-01564-5

Int. j. inf. tecnol.

2023

DOI: 10.1007/s41870-023-01564-5

|View full text |Cite

Leveraging contextual features to enhanced machine learning models in detecting COVID-19 fake news

Amal Esmail Qasem,

Mohammad Sajid

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

Pandey,

Nazarov

2023

Int. j. inf. tecnol.

View full text Add to dashboard Cite

Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

Pandey,

Nazarov

2023

Int. j. inf. tecnol.

View full text Add to dashboard Cite

CSSLnO: Cat Swarm Sea Lion Optimization-based deep learning for fake news detection from social media

Sirra,

Mogalla,

Madhuri

2024

Int. j. inf. tecnol.

View full text Add to dashboard Cite

Enhanced detection of fabricated news through sentiment analysis and text feature extraction

Narang,

Singh,

Monga

2024

Int. j. inf. tecnol.

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Leveraging contextual features to enhanced machine learning models in detecting COVID-19 fake news

Cited by 4 publications

References 22 publications

Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

CSSLnO: Cat Swarm Sea Lion Optimization-based deep learning for fake news detection from social media

Enhanced detection of fabricated news through sentiment analysis and text feature extraction

Contact Info

Product

Resources

About