Fake News Detection in Social Media Using Supervised Learning Techniques

The introduction of the internet and the quick adoption of public news platforms (such as Facebook(FB), Twitter and Instagram) prepared the door for unprecedented levels of knowledge distribution in human history. Thanks to social media platforms, consumers are creating and sharing more knowledge compare to before, Most of it is incorrect and has no bearing on the discussion. It's difficult to categorise a written work as misleading or disinformation using an algorithm. Even an expert in a given field must consider a variety of factors before deciding whether or not an item is true. For detecting spurious news, researchers recommend using a machine learning classification approach. Our research looks into different textual qualities that can be used to tell the difference between false and real content. We train a set of distinct machine learning algorithms using diverse integral approaches and evaluate their performance on real-world datasets using those properties. Our proposed ensemble learner method outperforms individual learners.