Fake News Detection using Machine Learning Algorithms and Recurrent
Neural Networks
Abstract
In recent years, fake news has been surfacing in enormous numbers and
spreading on the internet world for various political and commercial
purposes. One major reason for that huge number is the rapid growth of
social media. Online social network users can quickly get infected by
falling for deceptive phrases and words which already had a huge impact
on society. To combat this issue, Fact-checking, a method of evaluating
the credibility of online news articles is practiced but it usually
takes a longer time to process and flag fake news. By the time of
completion, the news would have already reached most of the audiences.
The vital goal in improving the credibility of online news is to
identify fake news promptly. The core purpose of this research is to
analyse, compare and evaluate the performance of multiple machine
learning and deep learning algorithms for detecting fake news. The idea
behind this research is to find the best performing model to identify
fake news, this is achieved by building and comparing the performance of
seven different models comprising five machine learning models and two
deep learning models. Accuracy, recall, precision, f1score and ROC curve
are calculated to evaluate the model’s performance. A web application
“Fake News Detector” is also built to distinguish the fake and true
news.