Leveraging Natural Language Processing for Detecting Fake News: A Comparative Analysis

Fake news have become a number one situation within the contemporary digital world, with the appearance and vast use of social media and remarkable online systems. The capability to quickly and without trouble disseminate fake records has brought about wrong information and its potential to affect public opinion and decision-making. As a result, there has been a growing interest in automatic strategies for detecting and preventing fake news. Natural language processing (NLP) techniques have proven promising in this regard, as they may look at the language and content of document articles to identify patterns and characteristics related to fake news. This paper compares several NLP strategies for detecting bogus records, including machine-learning-based, linguistics-based, and hybrid strategies. It compares the strategies' overall performance with excellent datasets and assessment metrics. Our results show that machine learning-based strategies and linguistics-based approaches outperform other strategies for detecting fake information.