Fake News Detection Using Machine Learning

Abstract

Abstract: The role of social media in our day to day life has increased rapidly in recent years. Information quality in social media is an increasingly important issue, but web-scale data hinders experts’ ability to assess and correct much of the inaccurate content, or “fake news”, present in these platforms. It is now used not only for social interaction, but also as an important platform for exchanging information and news. Twitter, Facebook a micro-blogging service, connects millions of users around the world and allows for the real-time propagation of information and news. The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destruction impacts. A lot of research is already focused on detecting it. A human being is unable to detect all these fake news. Detecting fake news is an important step. This process will result in feature extraction and vectorization; we propose using Python scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tiff Vectorizer. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest precision, according to confusion matrix results. A feature analysis then identifies features that are most predictive for crowdsourced and journalistic accuracy assessments, results of which are consistent with prior work. We aim to provide the user with the ability to classify the news as “fake” or “real”.