Analysis of different types of fake news and the forms of fact altering mechanisms

Abstract

Fake news is a fairly common phenomenon in the information and media landscape. It plays an active role in confirming the reader's beliefs and existing biases. From social media sites to news forums and blogs, the distinction between genuine and fake information is quietly blurring. Machine learning has taken a few strides in developing solutions that would distinguish between them. In our project, we would be utilizing large language models in natural language processing to study the distinctive features of fake news. The study would include the analysis of different types of fake news and the forms of fact altering mechanisms. We would be reviewing the existing detection models and disintegrating the aspects of fabricated news into identifying features. We would also be taking a few examples of fake news and testing the various approaches to establishing the veracity of the dataset.