A Data-Driven Framework for Fake News Detection Via Web Scraping and Machine Learning Approach

A great deal of misinformation has been circulated on a global scale in recent years due to the explosion of social media. The spread of false information has been worsened by recent political events. Some 1835 news stories were completely made up, like the one about "Bat-men on the moon." There has to be a system in place for checking claims, particularly those that get a lot of attention before being debunked by reliable sources. In order to properly categorize and identify fake news, a plethora of machine learning techniques have been used. The technique for spotting fake news inside datasets is the focus of this study. Online traditional news stories and news from other sources make up the bulk of the collection. The outcomes are compared to those of deep learning and traditional machine learning methods applied to the datasets, as well as long short-term memory (LSTM). Several example procedures are compared with the recommended methodology, and the results are given. In a number of respects, our work is superior than current methods. This approach has laid the groundwork for a system that can spot several red flags associated with fake news, classifying the material as either genuine or fraudulent and making decisions easier.