Fake news detection using naive bayes classifier and forward selection in the digital era

Traore Lei Ogilvie (Royal Thimphu College, Thimphu, Bhutan)
Monti Sharma (Aligarh Muslim University, Uttar Pradesh, INDIA)
Zhou Xei Huu (University of Computer Studies, Yangon, Myanmar)

Vertex
Vol. 11 No. 2 (2022): June: Engineering

This study investigates the application of the Naive Bayes Classifier Method with the Forward Selection Technique in an effort to detect hoax news. Through a hypothetical numerical example, this study illustrates the basic steps involved in this approach. The Naive Bayes method is used to estimate class probabilities based on the selected text features, while the Forward Selection technique is used to select the most informative features. The results and implications of this approach are discussed in the context of potential real-world applications. This research provides an initial understanding of the use of these techniques in dealing with the challenge of detecting fake news in the digital age. While this research does not describe the more complex aspects of real-world fake news detection, it highlights a foundation that can be developed for further efforts to improve information integrity in an increasingly complex digital environment.