Abstract
Fake news has been evolving into a problem that is getting even more challenging. Technology has been misused to spread false information about many things, such as war, pandemics, and the stock market. Unfortunately, this issue is not a big deal for some people without conscious consumption of that news. Hence, being part takes a role in combating the spread of false information using the advancement of technology. This study proposed two methods of machine learning model, Support Vector Machine (SVM) and Naïve Bayes, to classify fake news. Furthermore, to assert the applicability of models by examining news articles dataset which contain two labels, reliable and unreliable news. The higher accuracy is 0.96 using the SVM model
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering, MAthematics and Computer Science (EMACS) Journal
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
