Paper Contents
Abstract
Fake news identification has become one of the most important problems of the twenty-first century due to the sharp rise in social media use and the falling cost of internet access. The dissemination of rumors and false information has been greatly accelerated by this accessibility, leading to a rise in problems associated with fake news that range from online arguments to violent hate crimes. Despite its dependability, traditional fact-checking techniques are unable to handle the enormous amount of web content produced every day. Therefore, it is crucial to create an effective, scalable, and empirical approach to identify bogus news. In order to solve this issue, this study highlights machine learning (ML) and natural language processing (NLP) as crucial strategies. Real-time, automated news analysis and credibility-based classification are made possible by these technologies. This survey attempts to review previous research in order to compile knowledge, analyze current approaches, and determine their efficacy and correctness. Modelsadvantages, disadvantages, and areas for development are highlighted through comparison with industry standards. With accuracy as the key to success, the ultimate goal is to provide a template for a more successful false news detecting system. This study offers insightful information about how to use ML and NLP techniques to lessen the effects of false information and promote trust in online communication.
Copyright
Copyright © 2024 Bhargavi Jakkula. This is an open access article distributed under the Creative Commons Attribution License.
