An Automatic Fake News Detection System

201410575 Abass Mohammed  

201610656 Mohammad AlShamsi 

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 destructive impacts. A lot of research is already focused on detecting it.

This project makes an analysis of this problem related to fake news detection and explores the traditional machine learning models to choose the best, to create a model of a product with supervised machine learning algorithm, that can classify fake news as fake or real, by using tools like python scikit-learn, NLP for textual analysis. This process will result in feature extraction and vectorization; we propose using Python scikit-learn library to perform vectorization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tfidf Vectorizer. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest accuracy, according to score accuracy results.