Fake News Detection Using Machine Learning

Abstract

The phenomenon of fake news is expanding rapidly with the development of communication tools and social media. Fake news detection is
an emerging research area that is garnering a lot of interest. However, it faces some challenges due to limited resources such as datasets and
processing and analysis techniques.
In this work, we propose a fake news detection system using machine learning techniques. We used the term Frequency-Inverse Document
Frequency (TF-IDF) from Bag of Words and N-Grams as the feature extraction method, and Support Vector Machine (SVM) as the
classifier. We also propose fake news and true news datasets to train the proposed system. The results obtained demonstrate the efficiency of
the system.