Fake news detection using machine learning

The simple availability and exponential expansion of information available on social media networks has made distinguishing between false and real information difficult. More information is being generated and shared by consumers in these days; some are deceptive and has no bearing on reality during...

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Bibliographic Details
Published inAIP conference proceedings Vol. 2857; no. 1
Main Authors Kumaravel, Deepika, Natarajan, Sasirekha, Rajappa, Shyamala
Format Journal Article Conference Proceeding
LanguageEnglish
Published Melville American Institute of Physics 18.08.2023
Subjects
Algorithms
Availability
Classification
Digital media
Machine learning
News
Social networks
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Summary:The simple availability and exponential expansion of information available on social media networks has made distinguishing between false and real information difficult. More information is being generated and shared by consumers in these days; some are deceptive and has no bearing on reality during the use of social media platforms. Machine learning has played a significant role in information classification, but with some drawbacks. For news article classification, we propose using a machine learning ensemble technique. The experiment is carried out using the Passive Aggressive Classifier algorithm. We wish to offer people by giving the option of categorizing information as false or true, and also calculating the percentage of false articles.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0165193