Abstract: Fake news is a kind of propaganda in which false information is knowingly disseminated via news organizations and/or social media platforms. It is crucial to create methods of spotting false news information since its spread can have detrimental effects, such as influencing elections and widening political rifts. Today, the majority of people choose to obtain news online since it is convenient and affordable, ubiquitous, but this leads to the rapid spread of false information. News disseminated quickly among millions of users in a short amount of time as a result of the rise in the usage of social media platforms like Facebook, Twitter, etc. The effects of spreading false news are often much, from shaping public opinion to influencing election results in favour of particular politicians. With the use of the machine learning concept, this research seeks to perform binary classification of various news items that are available online. Additionally, it attempts to enable users to evaluate if a piece of news is true or false and to confirm the reliability of the website that is disseminating it.
Keywords: fake news, Machine Learning, machine learning, (ML Support Vector Machine (SVM), facebook, twitter.
Title: Machine Learning Approach to Fake News Detection
Author: Abiodun Abdulrazaq Ipaye, Adegbola Taofiq Adeola, Joda Shade Christiana, Abdulwahab Isiaka, Aibinuomo Ifemide, Uchechi Agbakwuru, Oyeneye Olawale
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 12, Issue 1, January 2024 - March 2024
Page No: 16-22
Research Publish Journals
Website: www.researchpublish.com
Published Date: 05-March-2024
