Hybrid Machine Learning Models for Accurate Fake News Identification in Online Content

The growing problem of fake news in online material has big effects on how people talk and trust each other. The use of hybrid machine learning models is a potential way to find and stop the spread of false information. This study looks at how combining several machine learning techniques can make finding fake news more accurate and reliable. Traditional algorithms like Support Vector Machines (SVM) and Decision Trees are combined with more modern ones like Neural Networks and Natural Language Processing (NLP) to create a full model that can understand both written and contextual information. The mixed model takes advantage of the best parts of each program, making up for the problems that some methods might have when used by themselves. A large set of different online news stories, including both real and fake ones, are used for a lot of experiments. The model is put through a lot of tests to see how well it does in a number of areas, such as accuracy, precision, memory, and F1 score. The results show that this method is much more accurate at finding fake news than single-way approaches, especially when it comes to finding the complex patterns of lying that are common in fake news. The mixed model works well in cross-domain uses, which shows that it can handle different kinds of online material. Issues like feature selection, model complexity, and computing speed are dealt with by using optimization methods. This makes sure that the mixed model can still be used in real time. The study stresses how important it is to combine different machine learning methods to make a more complete and useful system for finding fake news. Finding false information is a bigger topic, but this study adds to that discussion by creating a system that can be used on many platforms and languages. The results show that mixed models are very important for making automatic systems that find fake news more accurate and reliable.