Machine Learning for Fake News Detection Analysis

S., Abhilasha and R., Ushasree and Che Fuzlina, Mohd Fuad (2024) Machine Learning for Fake News Detection Analysis. Journal of Data Science, 2024 (08). pp. 1-9. ISSN 2805-5160

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Abstract

The COVID-19 outbreak has required some health and financial decisions to be made in an unwieldy manner. This has spread uncertainty and lies all over the world. The transmission of false information has been compounded by the problems with fake news. Many of them gave up on newspapers, magazines, and other print media in favor of Internet pleasure. Online entertainment has become the primary news source for a sizable percentage of the population due to its ease of access, low cost, and rapid spread. In some circumstances, bogus information spreads faster than true information to gain popularity over internet entertainment and divert people from the underlying issues. People spread false information using online entertainment for commercial and political benefit. To avoid a harmful influence on society, it is critical to immediately recognize bogus information in all systems. To demonstrate the efficiency of the grouping on the dataset, we produced and tested numerous AI computations independently for this assignment, which looks into research on the recognition of fake news. The Jupyter Notebook stage of this project was used, and the execution was assessed.

Item Type:	Article
Uncontrolled Keywords:	Fake News, Social Media, Machine Learning, and Classifiers
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	04 Jun 2024 07:34
Last Modified:	04 Jun 2024 07:34
URI:	http://eprints.intimal.edu.my/id/eprint/1923

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