Fake News Detection Using Machine Learning
6 Pages Posted: 3 Oct 2019
Date Written: October 2, 2019
Abstract
Nowadays most of the people prefer the internet to access news as it is easy and cheap, but that results in wide spreading of fake news very fast. Fake news is often written with an ulterior motive to gain financially, politically, etc. with most of the time having a catchy headline which attracts users or it may also be accidental. But it affects so much to the people. Fake news detection has become a challenging topic nowadays. In this work, we use the LIAR dataset which is collected from POLITIFACT.COM for fake news detection and it is publicly available for use, which provide links to source documents for each case. In all the previous works, the accuracies are all around 30 percent on this dataset. In this work, we use model ensemble techniques to have better accuracy in predicting fake news using the LIAR dataset. We have also tried to simplify the problem statement into binary classification and deployed the same ensemble techniques to have an even better realistic approach for accurate calculation.
Keywords: Ensemble, Fake News, Liar dataset, Classification, XGBoost
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